diff --git a/.gitignore b/.gitignore
index f5ec608..225fc6f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1 @@
-/*.log
-/*.png
 /__pycache__
-/*.json
-/.ipynb_checkpoints
-*.eps
diff --git a/Atlas.py b/Atlas.py
deleted file mode 100644
index 4ec75be..0000000
--- a/Atlas.py
+++ /dev/null
@@ -1,918 +0,0 @@
-'''
-Atlas: Exploration and Mapping with a Sparse Swarm of Networked IoT Robots
-'''
-
-#=== built-in
-import os
-import time
-import random
-import math
-import json
-import pprint
-import datetime
-#=== third-party
-#=== local
-import AtlasScenarios
-
-#============================ defines =========================================
-
-#=== settings
-
-SCENARIOS          = [
-    'scenario_floorplan',
-    'scenario_canonical',
-    'scenario_empty',
-    #'scenario_mini_floorplan',
-    #'scenario_mini_canonical',
-    #'scenario_mini_empty',
-    #'scenario_tiny_1',
-    #'scenario_tiny_2',
-]
-NUM_ROBOTS         = [10,20,30,40,50,60,70,80,90,100]
-NUMCYCLES          = 200
-UI                 = False
-
-#=== defines
-
-VERSION            = (1,3)
-
-HEADING_N          = 'N'
-HEADING_NE         = 'NE'
-HEADING_E          = 'E'
-HEADING_SE         = 'SE'
-HEADING_S          = 'S'
-HEADING_SW         = 'SW'
-HEADING_W          = 'W'
-HEADING_NW         = 'NW'
-HEADING_ALL        = [
-    HEADING_N, 
-    HEADING_NE,
-    HEADING_E,
-    HEADING_SE,
-    HEADING_S,
-    HEADING_SW,
-    HEADING_W,
-    HEADING_NW,
-]
-
-#============================ variables =======================================
-
-pp =  pprint.PrettyPrinter(depth=3,compact=True)
-
-#============================ helper functions ================================
-
-def genRealMapDrawing(drawing):
-    realMap   = []
-    startPos  = None
-    row       = 0
-    col       = 0
-    for line in drawing.splitlines():
-        if not line.startswith('#'):
-            continue
-        realMap += [[]]
-        for c in line:
-            if   c=='#':
-                realMap[-1] += [0]
-            elif c==' ':
-                realMap[-1] += [1]
-            elif c=='S':
-                realMap[-1] += [1]
-                assert startPos==None
-                startPos = (row,col)
-            else:
-                raise SystemError()
-            col   += 1
-        row  += 1
-        col   = 0
-    return (realMap,startPos)
-
-def printDiscoMap(discoMap,startPos,robotPositions,kpis):
-    output         = []
-    numUnExplored  = 0
-    output        += ['']
-    numRows        = len(discoMap)
-    numCols        = len(discoMap[0])
-    numCells       = numRows*numCols
-    for row in range(len(discoMap)):
-        line = []
-        for col in range(len(discoMap[row])):
-            while True:
-                # robot
-                robotFound = False
-                for (ri,(rx,ry)) in enumerate(robotPositions):
-                    if (row,col) == (rx,ry):
-                        robotFound = True
-                        line += [str(ri%10)]
-                        break
-                if robotFound:
-                    break
-                # start position
-                if (row,col)==startPos:
-                    line += ['S']
-                    break
-                # wall
-                if  discoMap[row][col]==0:
-                    line += ['#']
-                    break
-                # unexplored
-                if discoMap[row][col]==-1:
-                    numUnExplored += 1
-                    line += ['.']
-                    break
-                # explored
-                line += [' ']
-                break
-        output += [' '.join(line)]
-    output += ['']
-    output += [
-        'numExplored  : {0}/{1} ({2:.0f}%)'.format(
-            numCells-numUnExplored,numCells,100.0*((numCells-numUnExplored)/numCells)
-        )
-    ]
-    for k in sorted(kpis.keys()):
-        output += ['{0:<13}: {1}'.format(k,kpis[k])]
-    output = '\n'.join(output)
-    os.system('cls')
-    print(output)
-
-#============================ classes =========================================
-
-#======== exceptions
-
-class MappingDoneSuccess(Exception):
-    pass
-
-class MappingDoneIncomplete(Exception):
-    pass
-
-#======== navigation algorithms
-
-class Navigation(object):
-    def __init__(self,realMap,startPos,numRobots):
-        
-        # store params
-        self.realMap                   = realMap
-        self.startPos                  = startPos
-        self.numRobots                 = numRobots
-        
-        # local variablels
-        self.numRows                   = len(self.realMap)    # shorthand
-        self.numCols                   = len(self.realMap[0]) # shorthand
-        self.firstIteration            = True
-        self.rankMaps                  = {}
-        self.discoMap                  = []
-        self.allCellsIdx               = []
-        self.stats                     = {}
-        for (x,row) in enumerate(realMap):
-            self.discoMap             += [[]]
-            for (y,col) in enumerate(row):
-                self.discoMap[-1]     += [-1]
-                self.allCellsIdx      += [(x,y)]
-    
-    def getStats(self):
-        return self.stats
-    
-    def _moveRobot(self,robotIdx,newx,newy):
-        self.robotPositions[robotIdx] = (newx,newy)
-        self.robotsMoved             += [robotIdx]
-        
-        # update discoMap
-        for (x,y) in self._OneHopNeighborhood(newx,newy,shuffle=False):
-            if self.discoMap[x][y]==-1:
-                self.numExplored += 1
-            if   self.realMap[x][y] == 0:
-                self.discoMap[x][y]=0
-            elif self.realMap[x][y] == 1:
-                self.discoMap[x][y]=1
-            else:
-                raise SystemError()
-    
-    def _determineDoneExploring(self):
-        fullDiscoMap = True
-        for row in self.discoMap:
-            for cell in row:
-                if cell == -1:
-                    fullDiscoMap = False
-                    break
-        if fullDiscoMap:
-            raise MappingDoneSuccess
-    
-    def _OneHopNeighborhood(self,x,y,shuffle=True):
-        returnVal = []
-        for (nx,ny) in [
-                (x-1,y-1),(x-1,y  ),(x-1,y+1),
-                (x  ,y-1),          (x  ,y+1),
-                (x+1,y-1),(x+1,y  ),(x+1,y+1),
-            ]:
-            
-            # only consider cells inside the realMap
-            if  (
-                    (nx>=0)            and
-                    (nx<self.numRows)  and
-                    (ny>=0)            and
-                    (ny<self.numCols)
-                ):
-                returnVal += [(nx,ny)]
-        
-        if shuffle:
-            random.shuffle(returnVal)
-        return returnVal
-    
-    def _TwoHopNeighborhood(self,x,y):
-        returnVal = []
-        for (nx,ny) in [
-                (x-2,y-2),(x-2,y-1),(x-2,y  ),(x-2,y+1),(x-2,y+2),
-                (x-1,y-2),                              (x-1,y+2),
-                (x  ,y  ),                              (x  ,y+2),
-                (x+1,y-2),                              (x+1,y+2),
-                (x+2,y-2),(x+2,y-1),(x+2,y  ),(x+2,y+1),(x+2,y+2)
-            ]:
-            
-            # only consider cells inside the realMap
-            if  (
-                    (nx>=0)            and
-                    (nx<self.numRows)  and
-                    (ny>=0)            and
-                    (ny<self.numCols)
-                ):
-                returnVal += [(nx,ny)]
-        
-        random.shuffle(returnVal)
-        return returnVal
-
-#=== distributed
-
-class NavigationDistributed(Navigation):
-
-    def think(self, robotPositions):
-        
-        # initialize variables
-        self.robotPositions       = robotPositions[:] # make a local copy
-        self.robotsMoved          = []
-        self.numExplored          = 0
-        
-        # determine whether we're done exploring
-        self._determineDoneExploring()
-        
-        # move each robot
-        for (ridx,(rx,ry)) in enumerate(robotPositions):
-            
-            # determine validNextPositions
-            validNextPositions = []
-            for (nx,ny) in self._OneHopNeighborhood(rx,ry):
-                if  (
-                        (self.realMap[nx][ny]==1)   and         # no wall
-                        ((nx,ny) not in self.robotPositions)    # no robot
-                    ):
-                    validNextPositions += [(nx,ny)]
-            
-            # pick next position and move
-            if validNextPositions:
-                (newx,newy) = self._pickNextPosition(ridx,rx,ry,validNextPositions)
-                self._moveRobot(ridx,newx,newy)
-        
-        return (self.robotPositions,self.discoMap,self.numExplored)
-    
-    def _pickNextPosition(self,ridx,rx,ry,validNextPositions):
-        raise SystemError()
-    
-class NavigationRandomWalk(NavigationDistributed):
-    
-    def _pickNextPosition(self,ridx,rx,ry,validNextPositions):
-        return random.choice(validNextPositions)
-
-class NavigationBallistic(NavigationDistributed):
-
-    def __init__(self,realMap,startPos,numRobots):
-        NavigationDistributed.__init__(self,realMap,startPos,numRobots)
-        self.robotHeading = []
-        for _ in range(self.numRobots):
-            self.robotHeading += [random.choice(HEADING_ALL)]
-
-    def _pickNextPosition(self,ridx,rx,ry,validNextPositions):
-        
-        nextPosition = None
-        
-        while not nextPosition:
-            # compute next position
-            # FIXME: box
-            if   self.robotHeading[ridx]==HEADING_N:
-                nextPosition = (rx-1,ry  )
-            elif self.robotHeading[ridx]==HEADING_NE:
-                nextPosition = (rx-1,ry+1)
-            elif self.robotHeading[ridx]==HEADING_E:
-                nextPosition = (rx  ,ry+1)
-            elif self.robotHeading[ridx]==HEADING_SE:
-                nextPosition = (rx+1,ry+1)
-            elif self.robotHeading[ridx]==HEADING_S:
-                nextPosition = (rx+1,ry  )
-            elif self.robotHeading[ridx]==HEADING_SW:
-                nextPosition = (rx+1,ry-1)
-            elif self.robotHeading[ridx]==HEADING_W:
-                nextPosition = (rx  ,ry-1)
-            elif self.robotHeading[ridx]==HEADING_NW:
-                nextPosition = (rx-1,ry-1)
-            else:
-                raise SystemError()
-            
-            if nextPosition not in validNextPositions:
-                self.robotHeading[ridx] = random.choice(HEADING_ALL)
-                nextPosition = None
-        
-        return nextPosition
-
-#=== centralized
-
-class NavigationCentralized(Navigation):
-    
-    def __init__(self,realMap,startPos,numRobots):
-        Navigation.__init__(self,realMap,startPos,numRobots)
-        self.shouldvisits         = {}
-        self._distance(startPos) # force rankMap to be fully built for start position
-    
-    def _distance(self,pos1,pos2=None):
-        
-        # easy answer if same position
-        if pos1==pos2:
-            return 0
-        
-        # inverting pos1 and pos2 in case pos2 already cached (same distance)
-        if (pos1 not in self.rankMaps) and (pos2 in self.rankMaps):
-            temp = pos1
-            pos1 = pos2
-            pos2 = temp
-        
-        # check whether rankMaps in cache, otherwise build or resume
-        if  (
-                (pos1 in self.rankMaps) and
-                (pos2 in self.rankMaps[pos1])
-            ):
-                # stats
-                self._stats_incr('cache_hit')
-        else:
-            if  (
-                    (pos1 in self.rankMaps) and
-                    (pos2 not in self.rankMaps[pos1])
-                ):
-                # resuming building the rankMap
-                
-                # stats
-                self._stats_incr('cache_miss_resume')
-                
-                # local variables (resume)
-                rankMap                   = self.rankMaps[pos1]
-                shouldvisit               = self.shouldvisits[pos1]
-            else:
-                # starting new rankMap
-                
-                # stats
-                self._stats_incr('cache_miss_new')
-                
-                # local variables (new)
-                rankMap                   = {}
-                shouldvisit               = []
-                
-                # start from start position
-                rankMap[pos1]             = 0
-                shouldvisit              += [pos1]
-            
-            while True:
-                
-                # find cell to visit with lowest rank (abort if none)
-                found         = False
-                currentrank   = None
-                for (x,y) in shouldvisit:
-                    if  (
-                            currentrank==None or
-                            rankMap[(x,y)]<currentrank
-                        ):
-                        currentrank   = rankMap[(x,y)]
-                        (cx,cy)       = (x,y)
-                        found = True
-                if found==False:
-                    break
-                
-                # assign a height for all its neighbors
-                for (nx,ny) in self._OneHopNeighborhood(cx,cy):
-                    if (nx,ny) in rankMap:
-                        assert rankMap[(nx,ny)] <= currentrank+1 
-                    if  (
-                            (self.realMap[nx][ny]==1) and
-                            ((nx,ny) not in rankMap)
-                        ):
-                        rankMap[(nx,ny)]     = currentrank+1
-                        shouldvisit        += [(nx,ny)]
-                
-                # mark a visited
-                shouldvisit.remove((cx,cy))
-                
-                # abort if I reached pos2
-                if pos2 and (pos2 in rankMap):
-                    self.shouldvisits[pos1] = shouldvisit
-                    break
-            
-            self.rankMaps[pos1] = rankMap
-
-        if pos2:
-            return self.rankMaps[pos1][pos2]
-
-    def _stats_incr(self,k):
-        if k not in self.stats:
-            self.stats[k] = 0
-        self.stats[k] += 1
-
-class NavigationRamaithitima(NavigationCentralized):
-    
-    def __init__(self,realMap,startPos,numRobots):
-        NavigationCentralized.__init__(self,realMap,startPos,numRobots)
-        self.targetFrontierbots   = []
-    
-    def think(self, robotPositions):
-        
-        # initialize variables
-        self.robotPositions       = robotPositions[:] # make a local copy
-        self.robotsMoved          = []
-        self.numExplored          = 0
-        
-        # determine whether we're done exploring
-        self._determineDoneExploring()
-        
-        # step 0: move all non-frontier robot
-        catchup = False
-        moved   = True
-        while moved:
-            moved = self._moveNonFrontierBot()
-            if moved:
-                catchup = True
-        
-        if not catchup:
-            # step 1: move all frontier     robots
-            moved = True
-            while moved:
-                moved = self._moveFrontierBot()
-            self.targetFrontierbots = self._findFrontierBotIdxs()
-            
-            # step 2: move all non-frontier robot
-            moved = True
-            while moved:
-                moved = self._moveNonFrontierBot()
-        
-        # break if couldn't move any robot
-        if self.robotsMoved==[]:
-            raise MappingDoneIncomplete()
-        
-        return (self.robotPositions,self.discoMap,self.numExplored)
-    
-    def _moveFrontierBot(self):
-        moved = False
-        while True:
-            
-            # find all new frontier robots
-            frontierBotIdxs = self._findFrontierBotIdxs(exceptIdxs=self.robotsMoved)
-            if not frontierBotIdxs:
-                break
-            
-            # move the robot closest to the start position
-            self._moveClosestFrontierbot(frontierBotIdxs)
-            moved              = True
-            
-            break
-        return moved
-    
-    def _findFrontierBotIdxs(self,exceptIdxs=[]):
-        returnVal = []
-        
-        self.frontierBotNextHops = {}
-        
-        for (ridx,(rx,ry)) in enumerate(self.robotPositions):
-            
-            # don't consider robots in exceptIdxs
-            if ridx in exceptIdxs:
-                continue
-            
-            # check that robot has the frontier in its reachable 2-neighborhood
-            for (nx,ny) in self._TwoHopNeighborhood(rx,ry):
-                if  self.discoMap[nx][ny]==-1:
-                    nr       = set(self._OneHopNeighborhood(rx,ry))
-                    nn       = set(self._OneHopNeighborhood(nx,ny))
-                    inter    = nr.intersection(nn)
-                    nexthops = []
-                    for (x,y) in inter:
-                        if self.realMap[x][y]==1:
-                            nexthops += [(x,y)]
-                    if nexthops:
-                        self.frontierBotNextHops[ridx] = random.choice(nexthops)
-                        returnVal += [ridx]
-                        break
-        
-        return returnVal
-    
-    def _moveClosestFrontierbot(self,candidateRobotIdxs):
-        
-        # pick the robot closest to the start position
-        distanceToStart = {}
-        for ridx in candidateRobotIdxs:
-            (x,y) = self.robotPositions[ridx] # shorthand
-            distanceToStart[ridx] = self._distance(self.startPos,(x,y))
-        closestBotIdx = sorted(distanceToStart.items(), key=lambda item: item[1])[0][0]
-        (nx,ny) = self.frontierBotNextHops[closestBotIdx]
-        
-        # move the closestBot
-        self._moveRobot(closestBotIdx,nx,ny)
-    
-    def _moveNonFrontierBot(self):
-        moved = False
-        
-        # iterate through all robots until could move one
-        for (ridx,(rx,ry)) in enumerate(self.robotPositions):
-        
-            # break if no more frontierbots
-            if not self.targetFrontierbots:
-                break
-            
-            # don't consider frontier robots
-            if ridx in self.targetFrontierbots:
-                continue
-            
-            # don't consider robots already explored
-            if ridx in self.robotsMoved:
-                continue
-            
-            # pick a random closest frontier robot
-            distances = {}
-            for fidx in self.targetFrontierbots:
-                distances[fidx] = self._distance(self.robotPositions[ridx],self.robotPositions[fidx])
-            targetFrontierIdx = sorted(distances.items(), key=lambda item: item[1])[0][0]
-            
-            # attempt to move closer to target frontier robot
-            (fx,fy) = self.robotPositions[targetFrontierIdx] # shorthand
-            for (nx,ny) in self._OneHopNeighborhood(rx,ry):
-                if  (
-                        self.realMap[nx][ny]==1               and # open
-                        ((nx,ny) not in self.robotPositions)  and # no robots
-                        (nx,ny)!=self.startPos                and # not start position
-                        self._distance((nx,ny),(fx,fy))<self._distance((rx,ry),(fx,fy))
-                    ):
-                    
-                    # move robot
-                    self._moveRobot(ridx,nx,ny)
-                    moved         = True
-                    
-                    break
-        
-        return moved
-
-class NavigationAtlas(NavigationCentralized):
-    
-    def think(self, robotPositions):
-        
-        # initialize variables
-        self.robotPositions       = robotPositions[:] # make a local copy
-        self.robotsMoved          = []
-        self.numExplored          = 0
-        
-        # local variables
-        frontierCellsTargeted     = []
-        
-        (sx,sy)                   = self.startPos # shorthand
-        
-        # determine whether we're done exploring
-        try:
-            self._determineDoneExploring()
-        except:
-            self.stats['num_cache_entries'] = len(self.rankMaps)
-            l = [len(v) for (k,v) in self.rankMaps.items()]
-            self.stats['cache_maxlength']   = max(l)
-            self.stats['cache_minlength']   = min(l)
-            self.stats['cache_avglength']   = float(sum(l))/len(l)
-            raise
-        
-        #=== step 1. Move robots towards frontiercells
-        
-        while True:
-            if self.firstIteration:
-                # this is my first iteration: put robot 0 in the start position
-                
-                mr_idx                = 0
-                (mx_next,my_next)     = self.startPos
-                self.firstIteration   = False
-            
-            else:
-                # I already have robots in the area
-                
-                # identify all remaining frontierCells
-                allFrontierCellsAndDistance = []
-                for (x,y) in self.allCellsIdx:
-                    # don't consider the same call twice
-                    if (x,y) in frontierCellsTargeted:
-                        continue
-                    # consider only open cells
-                    if self.discoMap[x][y]!=1:
-                        continue
-                    # check wether this cell has unexplored neighbor cells
-                    for (nx,ny) in self._OneHopNeighborhood(x,y,shuffle=False):
-                        if self.discoMap[nx][ny]==-1:
-                            allFrontierCellsAndDistance += [((x,y),self._distance((sx,sy),(x,y)))]
-                            break
-                
-                # abort if no more frontier cells
-                if not allFrontierCellsAndDistance:
-                    break
-                
-                # keep only frontierCells with lowest rank
-                minRankFrontier = sorted(allFrontierCellsAndDistance, key=lambda item: item[1])[0][1]
-                frontierCells   = [c for (c,d) in allFrontierCellsAndDistance if d==minRankFrontier]
-                
-                # pick move robot (mv) and frontier cell (fc) to move towards
-                #   Rules (most important first):
-                #     - robot as close as possible to one of the target frontier cells
-                #     - robot as close as possible to start position
-                #     - frontier cell with many neighbors with a higher rank (avoids cutting corners)
-                #     - frontier cell with many unexplored neighbors
-                mr_idx                          = None
-                fc_pos                          = None
-                mr_distToStart                  = None
-                mr_distToFc                     = None
-                for (ridx,(rx,ry)) in enumerate(self.robotPositions):
-                    
-                    # don't move the same robot twice
-                    if ridx in self.robotsMoved:
-                        continue
-                    
-                    rDistToStart                = self._distance((sx,sy),(rx,ry))
-                    max_numHigherRankNeighbors  = None
-                    max_numUnexploredNeighbors  = None
-                    for (fx,fy) in frontierCells:
-                        rDistToFc               = self._distance((fx,fy),(rx,ry))
-                        (numHigherRankNeighbors,numUnexploredNeighbors)  = self._numHigherRankAndUnexploredNeighbors(fx,fy)
-                        if  (
-                                mr_idx==None                   or
-                                rDistToFc<mr_distToFc          or
-                                (
-                                    rDistToFc==mr_distToFc               and
-                                    rDistToStart<mr_distToStart
-                                )                              or
-                                (
-                                    rDistToFc==mr_distToFc               and
-                                    rDistToStart==mr_distToStart         and
-                                    max_numHigherRankNeighbors!=None     and
-                                    numHigherRankNeighbors>max_numHigherRankNeighbors
-                                )                              or
-                                (
-                                    rDistToFc==mr_distToFc               and
-                                    rDistToStart==mr_distToStart         and
-                                    max_numUnexploredNeighbors!=None     and
-                                    numUnexploredNeighbors>max_numUnexploredNeighbors
-                                )
-                            ):
-                            mr_idx                     = ridx
-                            fc_pos                     = (fx,fy)
-                            mr_distToStart             = rDistToStart
-                            mr_distToFc                = rDistToFc
-                            max_numHigherRankNeighbors = numHigherRankNeighbors
-                            max_numUnexploredNeighbors = numUnexploredNeighbors
-                
-                # abort if couldn't find robot to move
-                if mr_idx==None:
-                    break
-                
-                # pick new position
-                (mx_cur, my_cur)       = self.robotPositions[mr_idx] # shorthand
-                (fx,fy)                = fc_pos                      # shorthand
-                (mx_next,my_next)      = self._oneStepCloser(mx_cur,my_cur,fx,fy) 
-                
-                # abort if no way forward
-                if (mx_next,my_next)==(None,None):
-                    break
-                
-                frontierCellsTargeted += [fc_pos]
-            
-            # move moveRobot
-            self._moveRobot(mr_idx,mx_next,my_next)
-        
-        #=== step 2. Move remaining robots to center of gravity of frontiercells
-        
-        while True: # fake loop
-            
-            # identify all frontierCells
-            frontierCells = []
-            for (x,y) in self.allCellsIdx:
-                # consider only open cells
-                if self.discoMap[x][y]!=1:
-                    continue
-                # check wether this cell has unexplored neighbor cells
-                for (nx,ny) in self._OneHopNeighborhood(x,y,shuffle=False):
-                    if self.discoMap[nx][ny]==-1:
-                        frontierCells += [(x,y)]
-                        break
-            
-            # abort if no frontiercell (end of mapping)
-            if frontierCells==[]:
-                break
-            
-            # compute CG coordinates
-            cgxs = [x for (x,y) in frontierCells]
-            cgx  = int(float(sum(cgxs))/len(cgxs))
-            cgys = [y for (x,y) in frontierCells]
-            cgy  = int(float(sum(cgys))/len(cgys))
-            
-            # artificially move CG if falls on wall (distance calculation would fails)
-            if self.realMap[cgx][cgy]==0:
-                (cgx,cgy) = frontierCells[0]
-            
-            # move robots
-            for (ridx,(rx,ry)) in enumerate(self.robotPositions):
-                
-                # don't move the ones already moved in step 1
-                if ridx in self.robotsMoved:
-                    continue
-                
-                # pick new position
-                (xcur, ycur)       = self.robotPositions[ridx] # shorthand
-                (xnext,ynext)      = self._oneStepCloser(xcur,ycur,cgx,cgy)
-                
-                # abort if no way forward
-                if (xnext,ynext)==(None,None):
-                    continue
-                
-                # move
-                self._moveRobot(ridx,xnext,ynext)
-            
-            # end of fake loop
-            break
-        
-        return (self.robotPositions,self.discoMap,self.numExplored)
-    
-    def _oneStepCloser(self,sx,sy,tx,ty):
-        (nx,ny)    = (None,None)
-        min_dist   = None
-        for (x,y) in self._OneHopNeighborhood(sx,sy,shuffle=True):
-            if (
-                self.realMap[x][y]==1              and # no walls
-                (x,y) not in self.robotPositions       # no robots
-            ):
-                distToTarget = self._distance((x,y),(tx,ty))
-                if (
-                    min_dist==None or
-                    distToTarget<min_dist
-                ):
-                    min_dist = distToTarget
-                    (nx,ny) = (x,y)
-        return (nx,ny)
-    
-    def _numHigherRankAndUnexploredNeighbors(self,x,y):
-        numHigherRankNeighbors = 0
-        numUnexploredNeighbors = 0
-        rankMap = self.rankMaps[self.startPos] # shorthand
-        for (nx,ny) in self._OneHopNeighborhood(x,y,shuffle=False):
-            if  (
-                    self.discoMap[nx][ny]==1 and
-                    rankMap[(nx,ny)]>rankMap[(x,y)]
-                ):
-                numHigherRankNeighbors += 1
-            if self.discoMap[nx][ny]==-1:
-                numUnexploredNeighbors += 1
-        return (numHigherRankNeighbors,numUnexploredNeighbors)
-
-#======== core simulator
-
-'''
-calculates steps taken from source to destination
-'''
-
-def singleExploration(cycleId,scenarioName,realMap,startPos,NavAlgClass,numRobots,collectHeatmap):
-    navAlg                   = NavAlgClass(realMap,startPos,numRobots)
-    robotPositions           = [startPos]*numRobots
-    if collectHeatmap:
-        heatmap              = []
-        for (x,row) in enumerate(realMap):
-            heatmap         += [[]]
-            for (y,cell) in enumerate(row):
-                if cell==0:
-                    heatmap[-1]  += [-1]
-                else:
-                    heatmap[-1]  += [ 0]
-        (sx,sy)              = startPos
-    
-    numExplored          = 0
-    profile              = []
-    
-    kpis                     = {
-        'scenarioName': scenarioName,
-        'navAlg':       NavAlgClass.__name__,
-        'numTicks':     0,
-        'numSteps':     0,
-        'numRobots':    numRobots,
-        'version':      '.'.join([str(n) for n in VERSION]),
-    }
-    
-    while True:
-        
-        # think
-        try:
-            (nextRobotPositions,discoMap,numNewExplored) = navAlg.think(robotPositions)
-        except MappingDoneSuccess:
-            kpis['mappingoutcome'] = 'success'
-            break
-        except MappingDoneIncomplete:
-            kpis['mappingoutcome'] = 'incomplete'
-            break
-        
-        # move
-        for (i,(nx,ny)) in enumerate(nextRobotPositions):
-            (cx,cy) = robotPositions[i]
-            if (nx,ny) != (cx,cy):
-                kpis['numSteps'] += 1
-            if collectHeatmap:
-                assert heatmap[nx][ny]>=0
-                heatmap[nx][ny] += 1
-            robotPositions[i] = nextRobotPositions[i]
-        
-        # update KPIs
-        kpis['numTicks']    += 1
-        numExplored         += numNewExplored
-        profile             += [numExplored]
-        
-        # print
-        if UI:
-            printDiscoMap(discoMap,startPos,robotPositions,kpis)
-        
-        #input()
-    
-    if collectHeatmap:
-        kpis['heatmap']      = heatmap
-    kpis['profile']          = profile
-    kpis['navStats']         = navAlg.getStats()
-    
-    return kpis
-
-#============================ main ============================================
-
-def main():
-
-    NavAlgClasses  = [
-        NavigationRamaithitima,
-        NavigationAtlas,
-        NavigationRandomWalk,
-        NavigationBallistic,
-    ]
-    
-    startTime = time.time()
-    
-    with open('AtlasLog_{0}.json'.format(time.strftime("%y%m%d%H%M%S",time.localtime(startTime))).format(),'a') as f:
-    
-        for cycleId in range(NUMCYCLES):
-            
-            cycleStart = time.time()
-            
-            for numRobots in NUM_ROBOTS:
-                
-                if cycleId==0 and numRobots==max(NUM_ROBOTS):
-                    collectHeatmap = True
-                else:
-                    collectHeatmap = False
-                
-                for scenarioName in SCENARIOS:
-
-                    # create the realMap
-                    (realMap,startPos) = genRealMapDrawing(getattr(AtlasScenarios,scenarioName))
-                    
-                    # execute the simulation for each navigation algorithm
-                    for NavAlgClass in NavAlgClasses:
-                        
-                        # only 1 cycle for Atlas (deterministic)
-                        if NavAlgClass==NavigationAtlas and cycleId>0:
-                            continue
-                        
-                        kpis               = []
-                        
-                        # run single run
-                        start_time         = time.time()
-                        kpis               = singleExploration(cycleId,scenarioName,realMap,startPos,NavAlgClass,numRobots,collectHeatmap)
-                        print(
-                            'cycleId={0:>3} numRobots={1:>3} scenarioName={2:>30} NavAlgClass={3:>30} done in {4:>8.03f} s'.format(
-                                cycleId,
-                                numRobots,
-                                scenarioName,
-                                NavAlgClass.__name__,
-                                time.time()-start_time,
-                            )
-                        )
-                        
-                        # log KPIs
-                        kpis['cycleId']   = cycleId
-                        f.write(json.dumps(kpis)+'\n')
-                        f.flush()
-            
-            print(
-                '   full cycle {0:>3} done in {1:>10.03f} s (simulation has been running for {2})'.format(
-                    cycleId,
-                    time.time()-cycleStart,
-                    str(datetime.timedelta(seconds=time.time()-startTime)),
-                )
-            )
-    
-    print('Done.')
-
-if __name__=='__main__':
-    main()
diff --git a/AtlasScenarios.py b/AtlasScenarios.py
deleted file mode 100644
index c7e5c38..0000000
--- a/AtlasScenarios.py
+++ /dev/null
@@ -1,139 +0,0 @@
-scenario_empty = '''
-################################################################################
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              S
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-################################################################################
-'''
-
-scenario_canonical = '''
-################################################################################
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#          ##                    ##################################            #
-#          ##                    ##################################            #
-#          ##                                                                  #
-#          ##                                                                  #
-#          ##                                                                  #
-#          ##                                                                  #
-#          ##                                                                  S
-#          ##                                                                  #
-#          ##                                                                  #
-#          ##                                                                  #
-#          ##                    ##################################            #
-#          ##                    ##################################            #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-#                                                                              #
-################################################################################
-'''
-
-scenario_floorplan = '''
-################################################################################
-#          #            #            #                     #            #      #
-#          #            #            #                     #            #      #
-#          #            #            #                     #            #      #
-#          #            #            #                     #            #      #
-#          #            #            #                     #            #      #
-#     ##  ###   ##########  ############  ###################   ##########  ####
-#######                                                                        #
-#     #                                                                        #
-#                                                                              #
-#     #                                                                        #
-#######           #########  #############  #################                  S
-#     #                                                                        #
-#                                                                              #
-#     #                                                                        #
-#######                                                                        #
-#     ##  ###   ##########  ############  ###################   ##########  ####
-#          #            #            #                     #            #      #
-#          #            #            #                     #            #      #
-#          #            #            #                     #            #      #
-#          #            #            #                     #            #      #
-#          #            #            #                     #            #      #
-################################################################################
-'''
-
-scenario_mini_empty = '''
-###################
-#                 #
-#                 #
-#                 #
-#                 #
-#                 S
-#                 #
-#                 #
-#                 #
-#                 #
-###################
-'''
-
-scenario_mini_canonical = '''
-###################
-#                 #
-#                 #
-#   #    #####    #
-#   #             #
-#   #             S
-#   #             #
-#   #    #####    #
-#                 #
-#                 #
-###################
-'''
-
-scenario_mini_floorplan = '''
-###################
-#        #        #
-#        #        #
-###### #### #######
-#   #         #   #
-#      ####       S
-#   #         #   #
-###### #### #######
-#        #        #
-#        #        #
-###################
-'''
-
-scenario_tiny_1 = '''
-###########
-#         #
-#         #
-#         S
-#         #
-#         #
-###########
-'''
-
-scenario_tiny_2 = '''
-###########
-#         #
-#   #     #
-#   #     S
-#   #     #
-#         #
-###########
-'''
diff --git a/DotBot.py b/DotBot.py
new file mode 100644
index 0000000..06baeed
--- /dev/null
+++ b/DotBot.py
@@ -0,0 +1,378 @@
+# built-in
+import random
+import math
+import itertools
+import threading
+# third-party
+# local
+import SimEngine
+import Wireless
+import Utils as u
+
+class DotBot(object):
+    '''
+    A single DotBot.
+    '''
+    
+    def __init__(self,dotBotId,floorplan):
+        
+        # store params
+        self.dotBotId                  = dotBotId
+        self.floorplan                 = floorplan
+        
+        # local variables
+        self.simEngine                 = SimEngine.SimEngine()
+        self.wireless                  = Wireless.Wireless()
+        self.x                         = None  # the "real" position, sometimes in the past. Set to None to ensure single initialization
+        self.y                         = None
+        self.posTs                     = 0     # timestamp, in s, of when was at position
+        self.lastCommandIdReceived     = None  # set to None as not a valid command Id
+        self.headingRequested          = 0     # the heading, a float between 0 and 360 degrees (0 indicates North) as requested by the orchestrator
+        self.headingInaccuracy         = 0     # innaccuracy, in degrees of the heading. Actual error computed as uniform(-,+)
+        self.headingActual             = 0     # actual heading, taking into account inaccuracy
+        self.speedRequested            = 0     # speed, in m/s, as requested by the orchestrator
+        self.speedInaccuracy           = 0     # innaccuracy, in m/s of the speed. Actual error computed as uniform(-,+)
+        self.speedActual               = 0     # actual speed, taking into account inaccuracy
+        self.next_bump_x               = None  # coordinate the DotBot will bump into next
+        self.next_bump_y               = None
+        self.next_bump_ts              = None  # time at which DotBot will bump
+
+    #======================== public ==========================================
+        
+    def setInitialPosition(self,x,y):
+        '''
+        Call exactly once at start of simulation to exactly place the DotBot at its initial position.
+        '''
+        assert self.x==None
+        assert self.y==None
+        self.x      = x
+        self.y      = y
+        self.posTs  = self.simEngine.currentTime()
+    
+    def fromOrchestrator(self,packet):
+        '''
+        Received a packet from the orchestrator
+        '''
+        
+        # extract portion of orchestrator message which is for me (shorthand)
+        myMsg = packet[self.dotBotId]
+        
+        # disregard duplicate command
+        if myMsg['commandId']==self.lastCommandIdReceived:
+            return
+        
+        # remember what I was asked
+        self.lastCommandIdReceived     = myMsg['commandId']
+        self.headingRequested          = myMsg['heading']
+        self.speedRequested            = myMsg['speed']
+        
+        # apply heading and speed from packet
+        self._setHeading(myMsg['heading'])
+        self._setSpeed(  myMsg['speed'])
+        
+        # compute when/where next bump will happen
+        (bump_x,bump_y,bump_ts) = self._computeNextBump()
+
+
+
+
+        # remember
+        self.next_bump_x  = bump_x
+        self.next_bump_y  = bump_y
+        self.next_bump_ts = bump_ts
+        
+        # schedule
+        self.simEngine.schedule(self.next_bump_ts,self._bump)
+    
+    def getAttitude(self):
+        '''
+        "Backdoor" functions used by the simulation engine to compute where the DotBot is now.
+        
+        \post updates attributes position and posTs
+        '''
+        
+        # gather state
+        now              = self.simEngine.currentTime()
+        x                = self.x
+        y                = self.y
+        posTs            = self.posTs
+        headingActual    = self.headingActual
+        speedActual      = self.speedActual
+
+        # update position
+        newX                 = x + (now-posTs)*math.cos(math.radians(headingActual-90))*speedActual
+        newY                 = y + (now-posTs)*math.sin(math.radians(headingActual-90))*speedActual
+
+
+        # do NOT write back any results to the DotBot's state as race condition possible
+
+        return {
+            'x':           newX,
+            'y':           newY,
+            'heading':     self.headingActual,
+            'speed':       self.speedActual,
+            'next_bump_x': self.next_bump_x,
+            'next_bump_y': self.next_bump_y,
+        }
+    
+    #======================== private =========================================
+    
+    def _bump(self):
+        '''
+        Bump sensor triggered
+        '''
+        
+        # update my position
+        self.x               = self.next_bump_x
+        self.y               = self.next_bump_y
+        self.posTs           = self.next_bump_ts
+        # stop moving
+        self.speedActual     = 0
+
+        assert self.simEngine.currentTime() == self.next_bump_ts
+
+        # report bump to orchestrator
+        self.wireless.toOrchestrator({
+            'dotBotId':      self.dotBotId,
+            'bumpTs':        self.simEngine.currentTime(),
+
+        })
+    
+    def _setHeading(self,heading):
+        '''
+        Change the heading of the DotBot.
+        Actual heading affected by self.headingInaccuracy
+        Assumes applying new heading is infinitely fast.
+        '''
+        assert heading>=0
+        assert heading<360
+        if self.headingInaccuracy: # cut computation in two cases for efficiency
+            self.headingActual = heading + (-1+(2*random.random()))*self.headingInaccuracy
+        else:
+            self.headingActual = heading
+    
+    def _setSpeed(self,speed):
+        '''
+        Change the speed of the DotBot.
+        Actual speed affected by self.speedInaccuracy
+        Assumes applying new speed is infinitely fast.
+        '''
+        if self.speedInaccuracy: # cut computation in two cases for efficiency
+            self.speedActual = speed + (-1+(2*random.random()))*self.speedInaccuracy
+        else:
+            self.speedActual = speed
+
+    def _computeNextBump(self):
+
+        # compute when/where next bump will happen with frame
+        (bump_x_frame,bump_y_frame,bump_ts_frame) = self._computeNextBumpFrame()
+        
+        # start by considering you will bump into the frame
+        bump_x     = bump_x_frame
+        bump_y     = bump_y_frame
+        bump_ts    = bump_ts_frame
+        
+        # loop through obstables, lookign for closer bump coordinates
+        for obstacle in self.floorplan.obstacles:
+            
+            # coordinates of obstacble upper left and lower right corner
+            ax     = obstacle['x']
+            ay     = obstacle['y']
+            bx     = ax + obstacle['width']
+            by     = ay + obstacle['height']
+
+            # compute bump coordinate for this obstacle (if exist)
+            # Note: return (None,None,None) if no bump
+            (bump_xo,bump_yo,bump_tso)      = self._computeNextBumpObstacle(self.x,self.y,bump_x_frame,bump_y_frame,ax,ay,bx,by)
+
+            # update bump coordinates if closer
+            if (bump_xo!=None) and (bump_tso<=bump_ts):
+                    (bump_x,bump_y,bump_ts) = (bump_xo,bump_yo,bump_tso)
+
+        # FIXME: remove this
+        bump_x     = self.x + (bump_ts-self.posTs)*math.cos(math.radians(self.headingActual-90))*self.speedActual
+        bump_y     = self.y + (bump_ts-self.posTs)*math.sin(math.radians(self.headingActual-90))*self.speedActual
+        bump_x     = round(bump_x,3)
+        bump_y     = round(bump_y, 3)
+
+        # return where and when robot will bump
+        return (bump_x, bump_y ,bump_ts)
+
+    def _computeNextBumpFrame(self):
+        
+        if   self.headingActual in [ 90,270]:
+            # horizontal edge case
+            
+            north_x      = None # doesn't cross
+            south_x      = None # doesn't cross
+            west_y       = self.y
+            east_y       = self.y
+            
+        elif self.headingActual in [  0,180]:
+            # vertical edge case
+            
+            north_x     = self.x
+            south_x     = self.x
+            west_y      = None # doesn't cross
+            east_y      = None # doesn't cross
+        
+        else:
+            # general case
+             
+            # find equation of trajectory as y = a*x + b
+            a           = math.tan(math.radians(self.headingActual-90))
+            b           = self.y - (a*self.x)
+            
+            # compute intersection points with 4 walls
+            north_x     = (0                    -b)/a # intersection with North wall (y=0)
+            south_x     = (self.floorplan.height-b)/a # intersection with South wall (y=self.floorplan.height)
+            west_y      = 0*a+b                       # intersection with West wall (x=0)
+            east_y      = self.floorplan.width*a+b    # intersection with West wall (x=self.floorplan.width)
+
+            #round
+            north_x = round(north_x,3)
+            south_x = round(south_x,3)
+            west_y  = round(west_y ,3)
+            east_y  = round(east_y ,3)
+
+        # pick the two intersection points on the floorplan perimeter
+        valid_intersections = []
+        if (north_x!=None and 0<=north_x and north_x<=self.floorplan.width):
+            valid_intersections += [(             north_x,                    0)]
+        if (south_x!=None and 0<=south_x and south_x<=self.floorplan.width):
+            valid_intersections += [(             south_x,self.floorplan.height)]
+        if (west_y!=None  and 0<=west_y  and west_y<=self.floorplan.height):
+            valid_intersections += [(                   0,               west_y)]
+        if (east_y!=None  and 0<=east_y  and east_y<=self.floorplan.height):
+            valid_intersections += [(self.floorplan.width,               east_y)]
+        
+        # if more than 2 valid points, pick the pair that is furthest apart
+        if len(valid_intersections)>2:
+            distances = [(u.distance(a,b),a,b) for (a,b) in itertools.product(valid_intersections,valid_intersections)]
+            distances = sorted(distances,key = lambda e: e[0])
+            valid_intersections = [distances[-1][1],distances[-1][2]]
+        
+        assert len(valid_intersections)==2
+        
+        # pick the correct intersection point given the heading of the robot
+        (x_int0,y_int0) = valid_intersections[0]
+        (x_int1,y_int1) = valid_intersections[1]
+        if    self.headingActual==0:
+            # going up
+            
+            # pick top-most intersection
+            if y_int0<y_int1:
+                (bump_x,bump_y) = (x_int0,y_int0)
+            else:
+                (bump_x,bump_y) = (x_int1,y_int1)
+        elif ( 0<self.headingActual and self.headingActual<180 ):
+            # going right
+            
+            # pick right-most intersection
+            if x_int1<x_int0:
+                (bump_x,bump_y) = (x_int0,y_int0)
+            else:
+                (bump_x,bump_y) = (x_int1,y_int1)
+        elif  self.headingActual==180:
+            # going down
+            
+            # pick bottom-most intersection
+            if y_int1<y_int0:
+                (bump_x,bump_y) = (x_int0,y_int0)
+            else:
+                (bump_x,bump_y) = (x_int1,y_int1)
+        else:
+            # going left
+            
+            # pick right-most intersection
+            if x_int0<x_int1:
+                (bump_x,bump_y) = (x_int0,y_int0)
+            else:
+                (bump_x,bump_y) = (x_int1,y_int1)
+        
+        # compute time to bump
+        timetobump = u.distance((self.x,self.y),(bump_x,bump_y))/self.speedActual
+        bump_ts    = self.posTs+timetobump
+
+        # round
+        bump_x     = round(bump_x,3)
+        bump_y     = round(bump_y,3)
+        
+        return (bump_x,bump_y,bump_ts)
+    
+    def _computeNextBumpObstacle(self,rx,ry,x2,y2,ax,ay,bx,by):
+        '''
+        \param rx current robot coordinate x
+        \param ry
+        \param x2 second point on segment robot if traveling on
+        \param y2
+        \param ax upper-left corner of obstacle
+        \param ay
+        \param bx lower-right corner of obstacle
+        \param by
+        
+        \return (bump_x, bump_y,bump_ts) if robot bumps into obstacle
+        \return (  None,   None,   None) if robot does NOT bump into obstacle
+        
+        Function implements the Liang-Barsky algorithm algorithm
+        - https://www.ques10.com/p/22053/explain-liang-barsky-line-clipping-algorithm-with-/
+        - https://gist.github.com/ChickenProp/3194723
+        '''
+
+        # initial calculations (see algorithm)
+        deltax           = x2-rx
+        deltay           = y2-ry
+        #                         left      right     bottom        top
+        p                = [   -deltax,    deltax,   -deltay,    deltay ]
+        q                = [     rx-ax,     bx-rx,     ry-ay,     by-ry ]
+        
+        # initialize u1 and u2
+        u1               = -math.inf
+        u2               =  math.inf
+
+        # iterating over the 4 boundaries of the obstacle in order to find the t value for each one.
+        # if p = 0 then the trajectory is parallel to that boundary
+        # if p = 0 and q<0 then line completly outside boundaries
+
+        # update u1 and u2
+        for i in range(4):
+            
+            # abort if line outside of boundary
+            
+            
+            if p[i] == 0:
+                # line is parallel to boundary i
+                
+                if q[i]<0:
+                    return (None,None,None)
+                pass # nothing to do
+            else:
+                t = q[i] / p[i]
+                if   (p[i]<0 and u1<t):
+                    u1 = t
+                elif (p[i]>0 and u2>t):
+                    u2 = t
+
+        # if I get here, u1 and u2 should be set
+        assert u1 is not None
+        assert u2 is not None
+        
+        # decide what to return
+        if (u1>=0 and u1<=u2 and u2<=1):
+            
+            bump_x      = rx + u1 * deltax
+            bump_y      = ry + u1 * deltay
+
+
+            timetobump  = u.distance((rx, ry), (bump_x, bump_y)) / self.speedActual
+            bump_ts     = self.posTs + timetobump
+
+            #round
+            bump_x      = round(bump_x,3)
+            bump_y      = round(bump_y,3)
+
+            return (bump_x, bump_y,bump_ts)
+
+        else:
+
+            return (None,None,None)
diff --git a/DrawResults.ipynb b/DrawResults.ipynb
deleted file mode 100644
index 34e2bd8..0000000
--- a/DrawResults.ipynb
+++ /dev/null
@@ -1,830 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['AtlasLog_200501035706.json', 'AtlasLog_200501035831.json', 'AtlasLog_200504013412.json', 'AtlasLog_200504013536.json']\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import pprint\n",
-    "import random\n",
-    "import statistics\n",
-    "import scipy.stats\n",
-    "import numpy as np\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "import json\n",
-    "import AtlasScenarios\n",
-    "import glob\n",
-    "%matplotlib inline\n",
-    "\n",
-    "pp       =  pprint.PrettyPrinter(compact=True)\n",
-    "\n",
-    "LOGFILES = glob.glob('AtlasLog_*.json')\n",
-    "\n",
-    "print(LOGFILES)\n",
-    "\n",
-    "#=== line colors and styles\n",
-    "\n",
-    "linestyles = {\n",
-    "    # scenarios\n",
-    "    'empty':        'dotted',\n",
-    "    'canonical':    'dashed',\n",
-    "    'floorplan':    'solid',\n",
-    "    # algorithms\n",
-    "    'RandomWalk':   'dotted',\n",
-    "    'Ballistic':    'dashdot',\n",
-    "    'Ramaithitima': 'dashed',\n",
-    "    'Atlas':        'solid',\n",
-    "}\n",
-    "\n",
-    "linecolors = {\n",
-    "    # scenarios\n",
-    "    'empty':        '#262626',\n",
-    "    'canonical':    '#262626',\n",
-    "    'floorplan':    '#262626',\n",
-    "    # algorithms\n",
-    "    'RandomWalk':   '#666666',\n",
-    "    'Ballistic':    '#009900',\n",
-    "    'Ramaithitima': '#ff0000',\n",
-    "    'Atlas':        '#0000ff',\n",
-    "}\n",
-    "\n",
-    "#=== helper functions\n",
-    "\n",
-    "def mean_confidence_interval(data, confidence=0.95):\n",
-    "    a = 1.0 * np.array(data)\n",
-    "    n = len(a)\n",
-    "    m, se = np.mean(a), scipy.stats.sem(a)\n",
-    "    h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)\n",
-    "    return (m, h)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "numCycles: 145 numRuns: 13033\n"
-     ]
-    }
-   ],
-   "source": [
-    "#=== gather data\n",
-    "\n",
-    "log = []\n",
-    "for logfile in LOGFILES:\n",
-    "    with open(logfile,'r') as f:\n",
-    "        for line in f:\n",
-    "            log += [json.loads(line)]\n",
-    "\n",
-    "lastCycleId = None\n",
-    "numCycleIds = 0\n",
-    "for run in log:\n",
-    "    if run['cycleId']!=lastCycleId:\n",
-    "        numCycleIds += 1\n",
-    "        lastCycleId  = run['cycleId']\n",
-    "            \n",
-    "print('numCycles: {} numRuns: {}'.format(numCycleIds,len(log)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "floorplan Ramaithitima 126\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "floorplan Atlas 34\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "floorplan RandomWalk 750\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "floorplan Ballistic 155\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "canonical Ramaithitima 53\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "canonical Atlas 34\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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XjETH7mkTRefTkkDLJg6zn0P2fCKzbusVHTv7XZltd5R9vdZtMzZq3bSIv6gkSUNzoJIkDc2BSpI0NAcqSdLQmmfI5k+fX4/zkCQJ+YtKkjQ0BypJ0tAcqCRJQ3OgkiQNbW4ymTzqc5AkKeQvKknS0ByoJElDc6CSJA3NgUqSNDQHKknS0ByoJElD+x/IJ4jaxeXlCwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "canonical RandomWalk 367\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "canonical Ballistic 46\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "empty Ramaithitima 55\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "empty Atlas 34\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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Z9FzaTqW6vjQtNNNmZyT5NCO959W7XyUjt6VrrB1G6x7ajo6dblepWm5t2/UzbC1Pus20IWptLJn7uA4j+ZleI2030kJphP9RSZKW5kQlSVqaE5UkaWlOVJKkpQ1X4mgNEUmSDov/UUmSluZEJUlamhOVJGlpTlSSpKXtbTabP/scJEkq+R+VJGlpTlSSpKU5UUmSluZEJUlamhOVJGlpTlSSpKX9F8ah44pZ5vkQAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "empty RandomWalk 345\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "empty Ballistic 49\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaoAAACECAYAAADBXm8EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAO4ElEQVR4nO3dzWuV1xbH8Z0aU6ONoRpfUhDpiyIdagbBQSciXjLztpNS6D9yZ/1LCk7LhV7BV0TSWUEFJ5a2Qgl4SasmMZpYTa25kzs6+7fkt90ndlG+n+E6z/Ps/bycZ3PY66w9srm5WQAAyOqtv7oDAAC8CgMVACA1BioAQGoMVACA1BioAACpjb7qw5GZm6QEAgDeiM0bJ0ZUnF9UAIDUGKgAAKkxUAEAUmOgAgCk9spkCuW//5nein4M1bNnz2T8zz//rGIvXryoYqOj9WXZtm2bdTy1XSmlPH/+vIq9/fbbclunP6rfqj+v6tMwRW2rfm5sbLx2Ozt37rS3ffz4cRVT13JiYsI6XnSOg9Q5R3quhWqnpW3l0aNH1nbLy8tV7NChQ3Lb7du3V7GnT59a27lWVlZkfP/+/VXsjz/+sPZ/5513qtja2prdJ3Vv1THda/nFF19Usa+//lq27X5P1H1Q/XbPe2xszI7/419T1jFL4RcVACA5BioAQGoMVACA1BioAACpNSdTKLt27apibqJAKX2T/WqCWyUutPTp5cuXVeytt7wxvTdxwZ1QVhPCbh9LGX6CRZRooK6lOkeVALNjx46utt0EFrePiroP0b4tyTevq+U+qOdFXTM1ke4mHJXS9i5wqHOJkgeixKpB6p6pe6vOMfreqX4qav/vvvuuil2/fr2KffbZZ/KY33//vdW2el7c81bPxerqqmxHJTa14BcVACA1BioAQGoMVACA1BioAACpMVABAFIbStafmzFTip/JpbgZflGWkZthFfXdEWX6uFltPVlKLVqy1Qa52UwtbStuyaKW/rjbqvvgPj/RPVT7uxlWrpZnV2WbqUwuVfbn4cOHVWxyclK2o8r0uO8Btz/Reav77d5bdX1aSj+5mZa3b9+uYsePH69it27dqmLz8/Oy7TNnzlSxy5cvW31UMXV9VYafKllVSlTaq76WEX5RAQBSY6ACAKTGQAUASI2BCgCQWnMyhbveTTRZ6k7i9yROtExGq3Z6EhWitt1jumWQepMpVDs9paOi7XqSNpSWPraUlHK4z0pLSR21v3uOPfcr2l8lKqhjqsSJltJlbtKQ2x+V5FCKvy6T6o/qt3u/Ivfu3atiMzMzVUx9bz788ENru1JKuXDhQhVTCRbffPNNFVPv+KWlpSp24MCBKtb7XorwiwoAkBoDFQAgNQYqAEBqDFQAgNSGkkyhJtCiSTUVjyZCB6mJ1a1InHDXDVITsC2T2W5Vgp4+tnArJ7j9KaWvEonSMlnrJi8Mu0pHtK+6vu4z2bv+mNpfJRq4VRtUH1vWhFKJF+r77T5rat9S/ESHiYkJq52WZIqFhYUq9tFHH1n9Ue2srKxUsZZqIN9++20Vm52drWLnz5+vYu+//34Vc5/nuJ9UpgAA/E0wUAEAUmOgAgCkxkAFAEitOZlidNTbJZpUc0vI91SccJeHiPRMekd6lnNwJ9Kjf6m7k+5upQM1OR5dC/de9CZt9LTtVn1wRVUX3OfFTWhQ16xleR11fdzzVvu2VOSIluIZ9OTJkyoWJRC4VN9Vf9T5qCSQX3/9VbZz+PBhqz/uc67evdH9cpPWLl68WMU+//zzKnb16tUqpp7JKHnKrWgU4RcVACA1BioAQGoMVACA1BioAACpbdkyH1HJ/57ECTUZ2LLMgmrbnTx2J9yjCfzeqhFO2y2T2Yp7ji3/znePOezrE7XjVspo+df9oJb7oLbtWUoiOj/VjnvN3f5E33lVNULtrxIn3PdAdN5uBRc32UklTuzbt89uW73r3KQW9e6NkmfcxB113a5du1bFTp06ZW0Xtesm4UX4RQUASI2BCgCQGgMVACA1BioAQGp9M1z/pyb0oolnd2JWTWS61Riif727E+Q9FSPcf9yX4idj9Ezst2hJ0HC3c4/ZUwmiJZGjx1bch56KHL1LnijuNVtbW6ti7777rt0fdd6q4oSbQBVVA1FLj7iJE3fv3q1iqtpEbxKTSyWrRG2rJUF2795ttaOuuapMoRIs1HbDwC8qAEBqDFQAgNQYqAAAqTFQAQBSY6ACAKQ2lKy/lsw5N6OpJ8spKuXilqtxj9mzZlakJ8uut4xRT5mdlixP11asC9aT5am45Xha9ldZVxMTE9bxWtp2qf64GWSl+JmNisraczMBS4nfBYPu379fxVSGn8oujNbHctfeU8+AuyaZ6ncppUxPT8v4IPV9UOeztLRUxS5dulTFTp8+Lds5d+6c1Z8Iv6gAAKkxUAEAUmOgAgCkxkAFAEhtZHNzM/5w5mb14cK/67VX1Jomag2aSG8pokFRUoEq+6L6qfrjJhpE/e4pT+ROykZtqwlldx2vYZfeiWxFO+7EtXu/lZYyOe66Q+6z5pYUi6hnQE3OT01NVTF1LlHb6v3glp5yr8/vv/9uHa+UUhYXF6vYoUOHqpi79l601pJ7z1Tf1TF//vnnKnbs2DHZ9rDXOVP3S60fFl2LDz74oIqtH5uvYps3Toyo/flFBQBIjYEKAJAaAxUAIDUGKgBAas2VKX744YcqppIUNjY25P7uP8UVlSzQcjzVp+Xl5Sp28ODBKqbOscXY2Nhr76v6rfoTtaESRqL787r96Tm/UvxElxY959jTRnQtVNy9j73Pn7K+vv7a+6r1jqL1qNT5uPdG9VG1Ex3vxx9/tNpxqfdNVDVEJRvs3bu3iqm+37p1q4odOHCgikUVQtxKJi51Lr/99lsVU30sRZ/32s0ZsaVO7uMXFQAgNQYqAEBqDFQAgNQYqAAAqTUnU8zOzlYx9a/laDLP/Ue6ov5F7S4bUoqeCFXLCKgS++6/ureiQoNqW/VRVQBo6ZNbyaGlIkLPv+HfVLULxX1OW5YIcZco6el3xK1kopbQcJ/zludPHbPnmqvvQyn6+qoEql27dllttywzo85RvYPcRKKffvqpih09elS2rapqqKoRqm21nUqeUe/4qDKFWjJl5MSNKhbVSeIXFQAgNQYqAEBqDFQAgNQYqAAAqTUnU7jl49WkbCn+8h3uhLKaJI4m9lXbPRPc0SSqy227ZQLXpc6nJTFgUNQfNXms7sNWJBC41PPSc32jpAB1fYedVNDyvVNtqyUnxsfHrf5ECQ0qycJdWkVt5y5bE9m3r16q6MGDB1XMTbCI2naXXFFVG9Qx33vvvSoWvU9VxQo3oUY9f/v375ftDJqbm5PxhYWFKnb4n/U1j/CLCgCQGgMVACA1BioAQGoMVACA1JqTKVwtE4yqOoQ7yawmVqPlIVTb7kSv0jKB25M40VuhQVHHdJMpWqqLqHvrTqT3JHdE3MQJtz8tiRi9+zv7Rs+++j65+7tJDlFlCvcc1XPlvi+iZ8X97qjlKZaWlqqYmwxWiu67WqJEnbe6X+r6Tk5OyrZVUo26bqurq1VMVZxQfTx16lQVu3btmuzPL7/8IuMuflEBAFJjoAIApMZABQBIjYEKAJAaAxUAILXmrD83gycqiaO2VWuiqOwaN0spytpTmUFuBlvLulfDbtvdN7rmbyqjzuWur9XbR3d9LWXYGXot+7vZby3fO3V93YzM3mxQ1Y7KSlP7q0w3dd6964Kp/VWG3uLiYhWLsh3Vu8ltW53j+vp6FWtZB05l+Kl7o/p48uTJKjY/Py/b3gr8ogIApMZABQBIjYEKAJAaAxUAILXmZIoXL14MvRNq4u/+/ftVbHp6+rWPV4out/SmEg16EieUlvWbes7Hnfzt3d9d08wt5xNxt1Vtqz6q47WsR+X2xz3vx48fy/3VxL56hty1nloSS9S1fPToURUbHa1fRyqpquUZUOeo9lfn7SZ3RFQCi9rfXadMaXn2Vbkllch29uzZKnb9+nWr7egZ2NjYcLoY4hcVACA1BioAQGoMVACA1BioAACpDWU9qp5/s5eiJx2npqaq2MrKShVT/x6PuFUE1CSqu55US5KDmtR1K060JDm4iRzuxHNLRQ7FnQBWE+lqYj6a4HbvmZpIV//YV9Q1612zSN1HdR/URPju3bvtthW3UkbLOarrq+6ZuzZcS1JKz5pv6vqOj4/LdhT1vmqpZDKoJZHNXYdrbm6uil24cKGKuZWC1L0upZTl5WUR9Z9VflEBAFJjoAIApMZABQBIjYEKAJDaUJIpWpIK1GS4+se02l9NZKrjRRPh7gSjmiRU+7rJB5GWxItBvdUzevre0++Im2DhLhESbetWJ+lJYImuT88yIWqJhomJCauNFm4f1XYtCQ3us+ZWLIm+827ij3ou1PumJXlGJYSpBA1VkUM9V26llsgnn3xSxc6fP28dU1U8Uee9tLQk26YyBQDgb42BCgCQGgMVACA1BioAQGpDWeZDTTCqScNS/H/Ou8tvqEnU6N/R7r/p3X9hb8USIepauv+ub9HzD/k3tQyKW6Ehqkzx5MmTKqYSENxr6V6zKKnArXahJs337NlTxVoqRrgJMO53RJ1jdB/c75NbcUIlX/Um1KhlUNwlT1oqo7iJCqod9e6MnslPP/20il29erWKufdRxe7cuVPFjhw5IvuzsLAg4y5+UQEAUmOgAgCkxkAFAEiNgQoAkFpzMoWaVHP/bd3LTV6IJoTV5Kja1k1oULYiyaH3mFuR/NCjZ2kLJZpIV8kLw06K6ak2UUpblYVBLctdKO4SLqqP6hxblv5Q3zH3u9xyzd3r61Zqafkuuc+5SpJQ7yqVyKaSJkop5dy5c1WsJxlIxT7++OMqppY2aWk7wi8qAEBqDFQAgNQYqAAAqTFQAQBSY6ACAKTWnJqn1htR2SgRtS6JysJRx1T7jo2N2W27/XGptqNsR3U+alv3Wvau7+JS/VGxKNusJyuudx0kl/tc9T5/KjtWle5R3Pvd8lyovi8uLlYxlXmn+h2dizpv1U+1v3s+Uck29z665+NmZEbUd959D3z55ZdV7KuvvpLtXLlypYq510JRJbxavg/6/tTrfUX4RQUASI2BCgCQGgMVACA1BioAQGojm5ub8YczN+MPAQAYos0bJ0ZUnF9UAIDUGKgAAKkxUAEAUmOgAgCk9spkCgAA/mr8ogIApMZABQBIjYEKAJAaAxUAIDUGKgBAagxUAIDU/gcij12eQfZ4hwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\newcommand{\\heatmapMaxNumRobotsfloorplanRamaithitima}         {126\\xspace}\n",
-      "\\newcommand{\\heatmapMaxNumRobotsfloorplanAtlas}         {34\\xspace}\n",
-      "\\newcommand{\\heatmapMaxNumRobotsfloorplanRandomWalk}         {750\\xspace}\n",
-      "\\newcommand{\\heatmapMaxNumRobotsfloorplanBallistic}         {155\\xspace}\n",
-      "\\newcommand{\\heatmapMaxNumRobotscanonicalRamaithitima}         {53\\xspace}\n",
-      "\\newcommand{\\heatmapMaxNumRobotscanonicalAtlas}         {34\\xspace}\n",
-      "\\newcommand{\\heatmapMaxNumRobotscanonicalRandomWalk}         {367\\xspace}\n",
-      "\\newcommand{\\heatmapMaxNumRobotscanonicalBallistic}         {46\\xspace}\n",
-      "\\newcommand{\\heatmapMaxNumRobotsemptyRamaithitima}         {55\\xspace}\n",
-      "\\newcommand{\\heatmapMaxNumRobotsemptyAtlas}         {34\\xspace}\n",
-      "\\newcommand{\\heatmapMaxNumRobotsemptyRandomWalk}         {345\\xspace}\n",
-      "\\newcommand{\\heatmapMaxNumRobotsemptyBallistic}         {49\\xspace}\n"
-     ]
-    }
-   ],
-   "source": [
-    "'''\n",
-    "###############################################################################\n",
-    "Heatmap\n",
-    "###############################################################################\n",
-    "'''\n",
-    "\n",
-    "#=== create plot\n",
-    "\n",
-    "maxNumRobots = {}\n",
-    "\n",
-    "plotsgenerated = []\n",
-    "for run in log:\n",
-    "\n",
-    "    # shorthands\n",
-    "    scenarioName = run['scenarioName'][len('scenario_'):]\n",
-    "    navAlg       = run['navAlg'][len('Navigation'):]\n",
-    "    \n",
-    "    # don't plot same data twice\n",
-    "    if (\n",
-    "        ((scenarioName,navAlg) in plotsgenerated) or\n",
-    "        ('heatmap' not in run)\n",
-    "    ):\n",
-    "        continue\n",
-    "    \n",
-    "    # color cells visited\n",
-    "    data         = np.array(run['heatmap'])\n",
-    "    flat         = data.flatten()\n",
-    "    flat.sort()\n",
-    "    vmax         = flat[-2] # avoid counting num robots on start position\n",
-    "    maxNumRobots[(scenarioName,navAlg)] = vmax\n",
-    "    cmap         = plt.cm.Greys\n",
-    "    norm         = plt.Normalize(0, vmax)\n",
-    "    rgba         = cmap(norm(data))\n",
-    "    \n",
-    "    print(scenarioName,navAlg,vmax)\n",
-    "    \n",
-    "    # color real map\n",
-    "    realMap      = getattr(AtlasScenarios,'scenario_{}'.format(scenarioName))\n",
-    "    x = 0\n",
-    "    for line in realMap.splitlines():\n",
-    "        if not line.startswith('#'):\n",
-    "            continue\n",
-    "        for (y,c) in enumerate(line):\n",
-    "            if   c=='#':\n",
-    "                rgba[x, y, :3] = ( 0/255,  51/255, 204/255)\n",
-    "            elif c=='S':\n",
-    "                rgba[x, y, :3] = (1,0,0) #(255/255,   0/255,   0/255)\n",
-    "        x += 1\n",
-    "    \n",
-    "    (fig,ax) = plt.subplots()\n",
-    "    im       = ax.imshow(rgba, interpolation='nearest')\n",
-    "\n",
-    "    fig.tight_layout(pad=0, h_pad=0, w_pad=0)\n",
-    "    plt.axis('off')\n",
-    "    fig.savefig(\n",
-    "        os.path.join(\n",
-    "            'plots',\n",
-    "            'heatmap_{0}_{1}.eps'.format(\n",
-    "                scenarioName,\n",
-    "                navAlg,\n",
-    "            ),\n",
-    "        ),\n",
-    "        bbox_inches='tight',\n",
-    "    )\n",
-    "    plt.show()\n",
-    "    \n",
-    "    # remember what I have plotted\n",
-    "    plotsgenerated += [(scenarioName,navAlg)]\n",
-    "\n",
-    "for ((scenarioName,navAlg),num) in maxNumRobots.items():\n",
-    "    print('\\\\newcommand{{\\\\heatmapMaxNumRobots{}{}}}         {{{}\\\\xspace}}'.format(scenarioName,navAlg,num))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
-     ]
-    }
-   ],
-   "source": [
-    "'''\n",
-    "###############################################################################\n",
-    "Completion Ratio\n",
-    "###############################################################################\n",
-    "'''\n",
-    "\n",
-    "#=== gather data\n",
-    "\n",
-    "data     = {}\n",
-    "for run in log:\n",
-    "    navAlg           = run['navAlg']\n",
-    "    if navAlg!='NavigationRamaithitima':\n",
-    "        continue\n",
-    "    scenarioName     = run['scenarioName'][len('scenario_'):]\n",
-    "    numRobots        = run['numRobots']\n",
-    "    mappingoutcome   = run['mappingoutcome']\n",
-    "    if scenarioName not in data:\n",
-    "        data[scenarioName]             = {}\n",
-    "    if numRobots not in data[scenarioName]:\n",
-    "        data[scenarioName][numRobots]  = []\n",
-    "    if mappingoutcome=='success':\n",
-    "        data[scenarioName][numRobots] += [1]\n",
-    "    else:\n",
-    "        data[scenarioName][numRobots] += [0]\n",
-    "        \n",
-    "#=== create plot\n",
-    "\n",
-    "(fig,ax) = plt.subplots()\n",
-    "lines    = {}\n",
-    "for scenario in data.keys():\n",
-    "    x    = []\n",
-    "    y    = []\n",
-    "    yerr = []\n",
-    "    for (k,v) in data[scenario].items():\n",
-    "        x    += [k]\n",
-    "        (m,h) = mean_confidence_interval(v)\n",
-    "        y    += [m]\n",
-    "        yerr += [h]\n",
-    "    lines[scenario] = ax.errorbar(x, y, yerr=yerr,linestyle=linestyles[scenario],color=linecolors[scenario],label=scenario)\n",
-    "ax.grid(True, zorder=5)\n",
-    "plt.xlabel('number of robots')\n",
-    "plt.ylim(-0.1,1.1)\n",
-    "plt.ylabel('mapping completion ratio')\n",
-    "plt.legend(handles=[lines['empty'],lines['canonical'],lines['floorplan']])\n",
-    "plt.show()\n",
-    "fig.savefig(os.path.join('plots','completion_ratio.eps'),bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\twatteyn\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  keepdims=keepdims)\n",
-      "C:\\Users\\twatteyn\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  ret = ret.dtype.type(ret / rcount)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
-     ]
-    }
-   ],
-   "source": [
-    "'''\n",
-    "###############################################################################\n",
-    "Mapping Profile\n",
-    "###############################################################################\n",
-    "'''\n",
-    "\n",
-    "#=== gather data\n",
-    "\n",
-    "data               = {}\n",
-    "scenarioNames      = []\n",
-    "allNumRobots       = []\n",
-    "for run in log:\n",
-    "    scenarioName   = run['scenarioName'][len('scenario_'):]\n",
-    "    scenarioNames += [scenarioName]\n",
-    "    numRobots      = run['numRobots']\n",
-    "    allNumRobots  += [numRobots]\n",
-    "    navAlg         = run['navAlg'][len('Navigation'):]\n",
-    "    profile        = run['profile']\n",
-    "    if (scenarioName,numRobots) not in data:\n",
-    "        data[(scenarioName,numRobots)] = {}\n",
-    "    if navAlg not in data[(scenarioName,numRobots)]:\n",
-    "        data[(scenarioName,numRobots)][navAlg] = []\n",
-    "    data[(scenarioName,numRobots)][navAlg] += [profile]\n",
-    "    \n",
-    "for (k1,v1) in data.items():\n",
-    "    for (k2,profiles) in v1.items():\n",
-    "        maxlen  = max(len(p) for p in profiles)\n",
-    "        ms      = []\n",
-    "        hs      = []\n",
-    "        for i in range(maxlen):\n",
-    "            l = []\n",
-    "            for p in profiles:\n",
-    "                try:\n",
-    "                    l += [p[i]]\n",
-    "                except IndexError:\n",
-    "                    pass\n",
-    "            (m,h) = mean_confidence_interval(l)\n",
-    "            ms += [m]\n",
-    "            hs += [h]\n",
-    "        data[k1][k2] = {\n",
-    "            'mean':                ms,\n",
-    "            'confidence_interval': hs,\n",
-    "        }\n",
-    "    \n",
-    "#=== create plot\n",
-    "\n",
-    "scenarioNames = set(scenarioNames)\n",
-    "allNumRobots  = set(allNumRobots)\n",
-    "\n",
-    "for scenarioName in scenarioNames:\n",
-    "    (fig,ax) = plt.subplots()\n",
-    "    lines    = {}\n",
-    "    for ((scenario,numRobots),v) in data.items():\n",
-    "        if numRobots!=50:\n",
-    "            continue\n",
-    "        if scenario!=scenarioName:\n",
-    "            continue\n",
-    "        for (navAlg,profile) in v.items():\n",
-    "            x             = range(len(profile['mean']))\n",
-    "            y             = profile['mean']\n",
-    "            yerr          = profile['confidence_interval']\n",
-    "            lines[navAlg] = ax.errorbar(x=x,y=y,yerr=yerr,color=linecolors[navAlg],linestyle=linestyles[navAlg],label=navAlg)\n",
-    "    ax.grid(True, zorder=5)\n",
-    "    plt.xlabel('time (ticks)')\n",
-    "    plt.xlim(-100,2100)\n",
-    "    plt.ylabel('number discovered cells')\n",
-    "    plt.ylim(-100,2100)\n",
-    "    plt.legend(handles=[lines['Atlas'],lines['Ballistic'],lines['Ramaithitima'],lines['RandomWalk']])\n",
-    "    plt.show()\n",
-    "    fig.savefig(os.path.join('plots','mapping_profile_{0}.eps'.format(scenarioName)),bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
-      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
-     ]
-    }
-   ],
-   "source": [
-    "'''\n",
-    "###############################################################################\n",
-    "Mapping Speed\n",
-    "###############################################################################\n",
-    "'''\n",
-    "\n",
-    "#=== gather data\n",
-    "\n",
-    "data               = {}\n",
-    "scenarioNames      = []\n",
-    "incompletecases    = []\n",
-    "for run in log:\n",
-    "    # shorthands\n",
-    "    scenarioName   = run['scenarioName'][len('scenario_'):]\n",
-    "    scenarioNames += [scenarioName]\n",
-    "    navAlg         = run['navAlg'][len('Navigation'):]\n",
-    "    numRobots      = run['numRobots']\n",
-    "    mappingoutcome = run['mappingoutcome']\n",
-    "    numTicks       = run['numTicks']\n",
-    "    if mappingoutcome!='success':\n",
-    "        incompletecases += [(scenarioName,navAlg,numRobots)]\n",
-    "        continue\n",
-    "    if scenarioName not in data:\n",
-    "        data[scenarioName] = {}\n",
-    "    if navAlg not in data[scenarioName]:\n",
-    "        data[scenarioName][navAlg] = {}\n",
-    "    if numRobots not in data[scenarioName][navAlg]:\n",
-    "        data[scenarioName][navAlg][numRobots] = []\n",
-    "    data[scenarioName][navAlg][numRobots] += [numTicks]\n",
-    "\n",
-    "# remove cases where not all runs were successful\n",
-    "for (scenarioName,navAlg,numRobots) in set(incompletecases):\n",
-    "    try:\n",
-    "        del data[scenarioName][navAlg][numRobots]\n",
-    "    except KeyError:\n",
-    "        pass\n",
-    "\n",
-    "#=== create plot\n",
-    "\n",
-    "for scenarioName in data:\n",
-    "    (fig,ax) = plt.subplots()\n",
-    "    lines    = {}\n",
-    "    for navAlg in data[scenarioName]:\n",
-    "        x    = []\n",
-    "        y    = []\n",
-    "        yerr = []\n",
-    "        for numRobots in sorted(data[scenarioName][navAlg].keys()):\n",
-    "            l = data[scenarioName][navAlg][numRobots]\n",
-    "            if x and numRobots<x[-1]:\n",
-    "                print(scenarioName,navAlg,numRobots)\n",
-    "            x         += [numRobots]\n",
-    "            (m,h)      = mean_confidence_interval(l)\n",
-    "            y         += [m]\n",
-    "            yerr      += [h]\n",
-    "        lines[navAlg]  = ax.errorbar(x=x,y=y,yerr=yerr,color=linecolors[navAlg],linestyle=linestyles[navAlg],label=navAlg)\n",
-    "    ax.grid(True, zorder=5)\n",
-    "    plt.xlabel('number of robots')\n",
-    "    plt.xlim(0,103)\n",
-    "    plt.ylabel('ticks to complete mapping')\n",
-    "    plt.yscale('log')\n",
-    "    plt.ylim(10,100000)\n",
-    "    plt.legend(handles=[lines['Ramaithitima'],lines['RandomWalk'],lines['Ballistic'],lines['Atlas']])\n",
-    "    plt.show()\n",
-    "    fig.savefig(os.path.join('plots','mapping_speed_{0}.eps'.format(scenarioName)),bbox_inches='tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Floorplan.py b/Floorplan.py
new file mode 100644
index 0000000..568abf5
--- /dev/null
+++ b/Floorplan.py
@@ -0,0 +1,35 @@
+# built-in
+# third-party
+# local
+
+class Floorplan(object):
+    '''
+    The floorplan the DotBots move in.
+    '''
+    
+    def __init__(self,drawing):
+    
+        # local variables
+        (self.width,self.height,self.obstacles) = self._parseDrawing(drawing)
+    
+    #======================== public ==========================================
+    
+    def getJSON(self):
+        return {
+            'width':     self.width,
+            'height':    self.height,
+            'obstacles': self.obstacles,
+        }
+    
+    #======================== private =========================================
+    
+    def _parseDrawing(self,drawing):
+        lines     = [line for line in drawing.splitlines() if line]
+        width     = max([len(line) for line in lines])
+        height    = len(lines)
+        obstacles = []
+        for (y,line) in enumerate(lines):
+            for (x,c) in enumerate(line):
+                if c=='#':
+                    obstacles += [{'x': x, 'y':  y, 'width': 1, 'height': 1}]
+        return (width,height,obstacles)
\ No newline at end of file
diff --git a/Orchestrator.py b/Orchestrator.py
new file mode 100644
index 0000000..db57844
--- /dev/null
+++ b/Orchestrator.py
@@ -0,0 +1,374 @@
+# built-in
+import random
+import math
+import threading
+import copy
+import time
+# third-party
+# local
+import SimEngine
+import Wireless
+import Utils as u
+
+class ExceptionOpenLoop(Exception):
+    pass
+
+class MapBuilder(object):
+    '''
+    A background task which consolidates the map.
+    It combines dots into lines
+    It declares when the map is complete.
+    '''
+
+    PERIOD         = 1 # s, in simulated time
+    MINFEATURESIZE = 1 # shortest wall, narrowest opening
+
+    def __init__(self):
+
+        # store params
+
+        # local variables
+        self.simEngine       = SimEngine.SimEngine()
+        self.dataLock        = threading.RLock()
+        self.discoMap = {
+            'complete': False,    # is the map complete?
+            'dots':     [],       # each bump becomes a dot
+            'lines':    [],       # closeby dots are aggregated into a line
+        }
+
+        # schedule first housekeeping activity
+        self.simEngine.schedule(self.simEngine.currentTime()+self.PERIOD,self._houseKeeping)
+
+    #======================== public ==========================================
+
+    def notifBump(self,x,y):
+
+        with self.dataLock:
+            self.discoMap['dots'] += [(x,y)]
+
+    def getMap(self):
+
+        with self.dataLock:
+            return copy.deepcopy(self.discoMap)
+
+    #======================== private =========================================
+
+    def _houseKeeping(self):
+
+        with self.dataLock:
+            # consolidate map
+            self._consolidateMap()
+
+            # decide whether map completed
+            self.discoMap['complete'] = self._isMapComplete()
+
+        # schedule next consolidation activity
+        self.simEngine.schedule(self.simEngine.currentTime()+self.PERIOD,self._houseKeeping)
+
+    def _consolidateMap(self):
+
+        # result list of lines
+        reslines                             = []
+
+        # remove duplicate dots
+        self.discoMap['dots']                = list(set(self.discoMap['dots']))
+
+        # horizontal
+        for direction in ['horizontal','vertical']:
+
+            refs                             = []
+            if direction=='horizontal':
+                refs                        += [y   for (x,y)             in self.discoMap['dots']]               # all dots
+                refs                        += [lay for (lax,lay,lbx,lby) in self.discoMap['lines'] if lay==lby ] # all horizontal lines
+            else:
+                refs                        += [x   for (x,y)             in self.discoMap['dots']]               # all dots
+                refs                        += [lax for (lax,lay,lbx,lby) in self.discoMap['lines'] if lax==lbx ] # all vertical lines
+            refs                             = set(refs)
+
+            for ref in refs:
+
+                # select all the dots which are aligned at this ref
+                if direction=='horizontal':
+                    thesedots                = [x for (x,y) in self.discoMap['dots'] if y==ref]
+                else:
+                    thesedots                = [y for (x,y) in self.discoMap['dots'] if x==ref]
+
+                # select the lines we already know of at this ref
+                if direction=='horizontal':
+                    theselines               = [(lax,lay,lbx,lby) for (lax,lay,lbx,lby) in self.discoMap['lines'] if lay==ref and lby==ref]
+                else:
+                    theselines               = [(lax,lay,lbx,lby) for (lax,lay,lbx,lby) in self.discoMap['lines'] if lax==ref and lbx==ref]
+
+                # remove dots which fall inside a line
+                if direction=='horizontal':
+                    thesedots                = [x for (x,y) in self._removeDotsOnLines([(x,ref) for x in thesedots] ,theselines)]
+                else:
+                    thesedots                = [y for (x,y) in self._removeDotsOnLines([(ref,y) for y in thesedots] ,theselines)]
+
+                # add vertices of all lines to the dots
+                for (lax,lay,lbx,lby) in theselines:
+                    if direction=='horizontal':
+                        thesedots           += [lax]
+                        thesedots           += [lbx]
+                    else:
+                        thesedots           += [lay]
+                        thesedots           += [lby]
+
+                # remove duplicates (in case dot falls on vertice of existing line)
+                thesedots                    = list(set(thesedots))
+
+                # sort dots by increasing value
+                thesedots                    = sorted(thesedots)
+
+                # create line between close dots
+                for (idx,v) in enumerate(thesedots):
+                    if idx==len(thesedots)-1:
+                        continue
+                    vnext                    = thesedots[idx+1]
+
+                    if vnext-v<=self.MINFEATURESIZE:
+
+                        if direction=='horizontal':
+                            theselines      += [(v,ref,vnext,ref)]
+                        else:
+                            theselines      += [(ref,v,ref,vnext)]
+
+                # remove line duplicates (caused by short lines which turn into close points)
+                theselines                   = list(set(theselines))
+
+                # join the lines that touch
+                if direction=='horizontal':
+                    theselines = sorted(theselines,key = lambda l: l[0])
+                else:
+                    theselines = sorted(theselines,key = lambda l: l[1])
+                idx = 0
+                while idx<len(theselines)-1:
+                    (lax,lay,lbx,lby)        = theselines[idx]
+                    (nax,nay,nbx,nby)        = theselines[idx+1]
+                    if direction=='horizontal':
+                        condition            = (lbx==nax)
+                    else:
+                        condition            = (lby==nay)
+                    if condition:
+                        theselines[idx]      = (lax,lay,nbx,nby)
+                        theselines.pop(idx+1)
+                    else:
+                        idx                 += 1
+
+                # store
+                reslines                    += theselines
+
+        # store
+        self.discoMap['lines']               = reslines
+
+        # remove duplicate dots
+        self.discoMap['dots']                = list(set(self.discoMap['dots']))
+
+        # remove dots which fall inside a line
+        self.discoMap['dots']                = self._removeDotsOnLines(self.discoMap['dots'],self.discoMap['lines'])
+
+
+    def _removeDotsOnLines(self,dots,lines):
+        idx = 0
+        while idx<len(dots):
+            (dx,dy)                              = dots[idx]
+            removed                              = False
+            for (lax,lay,lbx,lby) in lines:
+                if   lay==lby and lay==dy:
+                    # horizontal line, co-linear to point
+
+                    condition                    = lax<=dx and dx<=lbx
+                elif lax==lbx and lax==dx:
+                    # vertical line,   co-linear to point
+
+                    condition                    = lay<=dy and dy<=lby
+                else:
+                    # not co-linear to point
+                    condition                    = False
+                if condition:
+                    dots.pop(idx)
+                    removed                      = True
+                    break
+            if removed==False:
+                idx                             += 1
+        return dots
+
+    def _isMapComplete(self):
+
+        while True: # "loop" only once
+
+            # map is never complete if there are dots remaining
+            if self.discoMap['dots']:
+                returnVal = False
+                break
+
+            # keep looping until no more todo lines
+            alllines = copy.deepcopy(self.discoMap['lines'])
+            try:
+
+                while alllines:
+                    loop      = self._walkloop(alllines,alllines[0])
+                    for line in loop:
+                        alllines.remove(line)
+
+            except ExceptionOpenLoop:
+                returnVal = False
+                break
+
+            # if I get here, map is complete
+            returnVal = True
+            break
+
+        return returnVal
+
+    def _walkloop(self,alllines,startline):
+
+        loop  = []
+        loop += [startline]
+        while True:
+            # add close line to loop
+            foundCloseLine = False
+            for line in alllines:
+                if (self._areLinesClose(loop[-1],line)) and (line not in loop):
+                    foundCloseLine = True
+                    loop          += [line]
+                    break
+
+            # abort if no next line to hop to
+            if foundCloseLine==False:
+
+                raise ExceptionOpenLoop()
+            # success! last line in loop is close to first line
+            if len(loop)>2 and self._areLinesClose(loop[-1],loop[0]):
+
+                return loop
+
+    def _areLinesClose(self,line1,line2):
+
+        (l1ax,l1ay,l1bx,l1by) = line1
+        (l2ax,l2ay,l2bx,l2by) = line2
+
+        returnVal = False
+
+        while True: # "loop" only once
+            if  u.distance((l1ax,l1ay),(l2ax,l2ay))<=self.MINFEATURESIZE:
+                returnVal = True
+                break
+            if  u.distance((l1ax,l1ay),(l2bx,l2by))<=self.MINFEATURESIZE:
+                returnVal = True
+                break
+            if  u.distance((l1bx,l1by),(l2ax,l2ay))<=self.MINFEATURESIZE:
+                returnVal = True
+                break
+            if  u.distance((l1bx,l1by),(l2bx,l2by))<=self.MINFEATURESIZE:
+                returnVal = True
+                break
+            break
+
+        return returnVal
+
+class Orchestrator(object):
+    '''
+    The central orchestrator of the expedition.
+    '''
+
+    def __init__(self,positions,floorplan):
+
+        # store params
+        self.positions         = positions
+        self.floorplan         = floorplan
+
+        # local variables
+        self.simEngine         = SimEngine.SimEngine()
+        self.wireless          = Wireless.Wireless()
+        self.dotbotsview       = [ # the Orchestrator's internal view of the DotBots
+            {
+                'x':           x,
+                'y':           y,
+                'posTs':       0,
+                'heading':     0,
+                'speed':       0,
+                'commandId':   0,
+            } for (x,y) in self.positions
+        ]
+        self.mapBuilder        = MapBuilder()
+
+    #======================== public ==========================================
+
+    def startExploration(self):
+        '''
+        Simulation engine, start exploring
+        '''
+        for dotbot in self.dotbotsview:
+            dotbot['heading'] = random.randint(0,359)
+            dotbot['speed']   = 1
+
+        self._sendDownstreamCommands()
+
+    def fromDotBot(self,msg):
+        '''
+        A DotBot indicates its bump sensor was activated at a certain time
+        '''
+        
+        # shorthand
+        dotbot               = self.dotbotsview[msg['dotBotId']]
+        
+        # compute new theoretical position
+        dotbot['x']         += (msg['bumpTs']-dotbot['posTs'])*math.cos(math.radians(dotbot['heading']-90))*dotbot['speed']
+        dotbot['y']         += (msg['bumpTs']-dotbot['posTs'])*math.sin(math.radians(dotbot['heading']-90))*dotbot['speed']
+        dotbot['posTs']      = msg['bumpTs']
+
+        # round
+        dotbot['x']          = round(dotbot['x'],3)
+        dotbot['y']          = round(dotbot['y'],3)
+
+        # notify the self.mapBuilder the obstacle location
+        self.mapBuilder.notifBump(dotbot['x'],dotbot['y'])
+
+        # adjust the heading of the DotBot which bumped (avoid immediately bumping into the same wall)
+        dotbot['heading']    = random.randint(  0,359)
+
+        # set the DotBot's speed
+        dotbot['speed']      = 1
+
+        # bump command Id so DotBot knows this is not a duplicate command
+        dotbot['commandId'] += 1
+
+        # send commands to the robots
+        self._sendDownstreamCommands()
+
+    def getView(self):
+
+        # do NOT write back any results to the DotBot's state as race condition possible
+
+        # compute updated position
+        now         = self.simEngine.currentTime() # shorthand
+
+        return {
+            'dotbots': [
+                {
+                    'x': db['x']+(now-db['posTs'])*math.cos(math.radians(db['heading']-90))*db['speed'],
+                    'y': db['y']+(now-db['posTs'])*math.sin(math.radians(db['heading']-90))*db['speed'],
+                } for db in self.dotbotsview
+            ],
+            'discomap': self.mapBuilder.getMap(),
+        }
+
+    #======================== private =========================================
+
+    def _sendDownstreamCommands(self):
+        '''
+        Send the next heading and speed commands to the robots
+        '''
+
+        # format msg
+        msg = [
+            {
+                'commandId': dotbot['commandId'],
+                'heading':   dotbot['heading'],
+                'speed':     dotbot['speed'],
+            } for dotbot in self.dotbotsview
+        ]
+
+        # hand over to wireless
+        self.wireless.toDotBots(msg)
diff --git a/PROTOCOL.md b/PROTOCOL.md
new file mode 100644
index 0000000..d19fa44
--- /dev/null
+++ b/PROTOCOL.md
@@ -0,0 +1,31 @@
+    PROTOCOL.md
+
+    Robot specs:
+        Very small in size
+        Walking speed : 1 metre/second
+
+    Full cycle = 1 second : upstream -> move -> downstream
+
+    1. Upstream communication [robots to controller] 
+        a. Robots only communicate back to controller when they hit obstacle
+        [hit obstacle -> stop moving -> communicate this to controller]
+        b. Communicated Frame:
+            i.  contains timestamp
+            ii. Timing accuracy : 1 ms
+            iii. Size: 160 bits in total of which 4 bytes are data
+        c. Packet is communicated via constructive interference:
+            i. Communication time: 2ms per robot
+        d. Only one robot can report back at a time
+        e. Packet either goes through or not based on PDR value
+
+    2. Downstream communication:
+        a. Controller communicates headings (direction of movement) to all robots every second
+        b. Communication:
+            i.  happens via constructive interference
+            ii. Retransmitts 5 times
+            iii. Robots that retransmit have to do this within 500 ns for CI to work
+        c. Packet either goes through or not based on PDR value
+        d. One frame contains all the 'headings' for the robots
+            i. 4 bits per robot [2 robots per byte]
+            ii. 9 heading posibilities [N, NW,NE,S,SW,SE,E,W,same position]
+    255 bytes per packet -> this seets the limitation on the number of robots
diff --git a/README.md b/README.md
index 315a4d2..b4bb071 100644
--- a/README.md
+++ b/README.md
@@ -10,15 +10,8 @@ Each major release contains the source code, the log file captured after a long
 
 - Install [Python 3.x](https://www.python.org/downloads/)
 - Install Python dependencies: `pip install -r requirements.txt`
-- start a simulation: `python Atlas.py`
-- Depending on the settings of the simulation, this can take several hours. The simulation ends with "Done."
-- When finished, `AtlasLog_<timestamp>.json` contains the following log file for all runs
-- Open `DrawResults.ipynb` using [Jupyter Notebook](https://jupyter.org/) to re-create the plots.
-
-## Turning on the Map View
-
-In the `Atlas.py`, set `UI` to `True` to turn on the map view.
-This significantly slows down the simulation.
+- start a simulation: `python RunSim.py`
+- once mapping finished, boundary line turns green
 
 ## Contributors
 
diff --git a/RunSim.py b/RunSim.py
new file mode 100644
index 0000000..fb5f4f4
--- /dev/null
+++ b/RunSim.py
@@ -0,0 +1,75 @@
+# built-in
+# third-party
+# local
+import Floorplan
+import DotBot
+import Orchestrator
+import Wireless
+import SimEngine
+import SimUI
+
+#============================ defines =========================================
+
+SIMSETTINGS = [
+    {
+        'numDotBots':       50,
+        'floorplanDrawing': # 1m per character
+'''
+............###...
+..................
+....##.....##.....
+....##............
+..............##..
+..............##..
+''',
+        'initialPosition':  (5,1),
+    }
+]
+#============================ helpers =========================================
+
+def oneSim(simSetting):
+    '''
+    Run a single simulation.
+    '''
+    
+    # create the wireless communication
+    wireless       = Wireless.Wireless()
+    
+    # create the SimEngine
+    simEngine      = SimEngine.SimEngine()
+    
+    # create the floorplan
+    floorplan      = Floorplan.Floorplan(simSetting['floorplanDrawing'])
+    
+    # create the DotBots
+    dotBots        = []
+    for dotBotId in range(simSetting['numDotBots']):
+        dotBots   += [DotBot.DotBot(dotBotId,floorplan)]
+    
+    # drop the DotBots on the floorplan at their initial position
+    (x,y) = simSetting['initialPosition']
+    for dotBot in dotBots:
+        dotBot.setInitialPosition(x,y)
+    
+    # create the orchestrator
+    orchestrator   = Orchestrator.Orchestrator([simSetting['initialPosition']]*len(dotBots),floorplan)
+    
+    # indicate the elements to the singletons
+    wireless.indicateElements(dotBots,orchestrator)
+    
+    # start the UI (call last)
+    simUI          = SimUI.SimUI(floorplan,dotBots,orchestrator)
+    
+    # schedule the first event
+    simEngine.schedule(0,orchestrator.startExploration)
+    
+    input('Press Enter to close simulation.')
+
+#============================ main ============================================
+
+def main():
+    for simSetting in SIMSETTINGS:
+        oneSim(simSetting)
+
+if __name__=='__main__':
+    main()
diff --git a/SimEngine.py b/SimEngine.py
new file mode 100644
index 0000000..cb9d887
--- /dev/null
+++ b/SimEngine.py
@@ -0,0 +1,168 @@
+# built-in
+import threading
+import time
+import datetime
+# third-party
+# local
+
+class SimEngine(threading.Thread):
+    '''
+    Discrete-event simulation engine for a swarm of DotBots.
+    '''
+    
+    MODE_PAUSE          = 'pause'
+    MODE_FRAMEFORWARD   = 'frameforward'
+    MODE_PLAY           = 'play'
+    MODE_FASTFORWARD    = 'fastforward'
+    
+    # singleton pattern
+    _instance   = None
+    _init       = False
+    def __new__(cls, *args, **kwargs):
+        if not cls._instance:
+            cls._instance = super(SimEngine, cls).__new__(cls, *args, **kwargs)
+        return cls._instance
+    
+    def __init__(self):
+        
+        # singleton patterm
+        if self._init:
+            return
+        self._init = True
+        
+        # local variables
+        self._currentTime         = 0    # what time is it for the DotBots
+        self._mode                = self.MODE_PAUSE
+        self._startTsSim          = None
+        self._startTsReal         = None
+        self._playSpeed           = 1.00
+        self.events               = []
+        self.semNumEvents         = threading.Semaphore(0)
+        self.dataLock             = threading.Lock()
+        self.semIsRunning         = threading.Lock()
+        self.semIsRunning.acquire()
+        
+        # start thread
+        threading.Thread.__init__(self)
+        self.name                 = 'SimEngine'
+        self.daemon               = True
+        self.start()
+    
+    #======================== thread ==========================================
+    
+    def run(self):
+        while True:
+            
+            # wait for simulator to be running
+            self.semIsRunning.acquire()
+            self.semIsRunning.release()
+            
+            # wait for at least one event
+            self.semNumEvents.acquire()
+            
+            # handle next event
+            (ts,cb) = self.events.pop(0)
+            assert self._currentTime<=ts
+            self._currentTime = ts
+            cb()
+            
+            # switch to MODE_PAUSE if in MODE_FRAMEFORWARD
+            if self._mode==self.MODE_FRAMEFORWARD:
+                self._mode=self.MODE_PAUSE
+                self.semIsRunning.acquire()
+            
+            # wait if in MODE_PLAY
+            if self._mode==self.MODE_PLAY:
+                durSim  = self._currentTime-self._startTsSim
+                durReal = time.time()-self._startTsReal
+                if durReal*self._playSpeed<durSim:
+                    time.sleep( durSim - (durReal*self._playSpeed) )
+    
+    #======================== public ==========================================
+    
+    #=== from other elements in simulator
+    
+    def currentTime(self):
+        return self._currentTime
+    
+    def mode(self):
+        return self._mode
+    
+    def formatSimulatedTime(self):
+        returnVal            = []
+        returnVal           += ['[']
+        returnVal           += [' {0} simulated'.format(str(datetime.timedelta(seconds=self._currentTime)).split('.')[0])]
+        if self._startTsSim!=None :
+            durSim           = self._currentTime-self._startTsSim
+            durReal          = time.time()-self._startTsReal
+            if durReal>1:
+                simSpeed     = int(durSim / durReal)
+            else:
+                simSpeed     = '?'
+            returnVal       += ['( {0:>4} &times; )'.format(simSpeed)]
+        returnVal           += [']']
+        returnVal            = ' '.join(returnVal)
+        return returnVal
+    
+    def schedule(self,ts,cb):
+        # add new event
+        self.events += [(ts,cb)]
+        
+        # reorder list
+        self.events  = sorted(self.events, key = lambda e: e[0])
+        
+        # release semaphore
+        self.semNumEvents.release()
+    
+    #=== commands from the GUI
+    
+    def commandPause(self):
+        '''
+        pause the execution of the simulation
+        '''
+        
+        with self.dataLock:
+            if self._mode != self.MODE_PAUSE:
+                self.semIsRunning.acquire()
+            self._startTsSim  = None
+            self._startTsReal = None
+            self._mode        = self.MODE_PAUSE
+    
+    def commandFrameforward(self):
+        '''
+        execute next event in list of events
+        '''
+        
+        with self.dataLock:
+            if self._mode == self.MODE_PAUSE:
+                self.semIsRunning.release()
+            self._startTsSim  = None
+            self._startTsReal = None
+            self._mode        = self.MODE_FRAMEFORWARD
+    
+    def commandPlay(self,playSpeed):
+        '''
+        (re)start the execution of the simulation at moderate simSpeed
+        '''
+        
+        with self.dataLock:
+            if self._mode == self.MODE_PAUSE:
+                self.semIsRunning.release()
+            self._startTsSim  = self._currentTime
+            self._startTsReal = time.time()
+            self._playSpeed   = playSpeed
+            self._mode        = self.MODE_PLAY
+    
+    def commandFastforward(self):
+        '''
+        (re)start the execution of the simulation at full simSpeed
+        '''
+        
+        with self.dataLock:
+            if self._mode == self.MODE_PAUSE:
+                self.semIsRunning.release()
+            self._startTsSim  = self._currentTime
+            self._startTsReal = time.time()
+            self._mode        = self.MODE_FASTFORWARD
+    
+    #======================== private =========================================
diff --git a/SimUI.py b/SimUI.py
new file mode 100644
index 0000000..bbc4d73
--- /dev/null
+++ b/SimUI.py
@@ -0,0 +1,123 @@
+# built-in
+import threading
+import webbrowser
+import time
+# third-party
+import bottle
+# local
+import SimEngine
+import SimVersion
+
+class SimUI(object):
+    '''
+    Web-based User Interface of the simulator.
+    '''
+    
+    TCPPORT = 8080
+    
+    def __init__(self,floorplan,dotbots,orchestrator):
+    
+        # store params
+        self.floorplan       = floorplan
+        self.dotbots         = dotbots
+        self.orchestrator    = orchestrator
+        
+        # local variables
+        self.simEngine       = SimEngine.SimEngine()
+        
+        # start web server
+        self.websrv          = bottle.Bottle()
+        self.websrv.route('/',                        'GET',    self._webhandle_root_GET)
+        self.websrv.route('/static/<filename>',       'GET',    self._webhandle_static_GET)
+        self.websrv.route('/floorplan.json',          'GET',    self._webhandle_floorplan_GET)
+        self.websrv.route('/dotbots.json',            'GET',    self._webhandle_dotbots_GET)
+        self.websrv.route('/frameforward',            'POST',   self._webhandle_frameforward_POST)
+        self.websrv.route('/play',                    'POST',   self._webhandle_play_POST)
+        self.websrv.route('/fastforward',             'POST',   self._webhandle_fastforward_POST)
+        self.websrv.route('/pause',                   'POST',   self._webhandle_pause_POST)
+        webthread = threading.Thread(
+            target = self._bottle_try_running_forever,
+            args   = (self.websrv.run,),
+            kwargs = {
+                'host'          : '127.0.0.1',
+                'port'          : self.TCPPORT,
+                'quiet'         : True,
+                'debug'         : False,
+            }
+        )
+        webthread.name       = 'WebServer'
+        webthread.daemon     = True
+        webthread.start()
+        
+        # open browser
+        webbrowser.open('http://127.0.0.1:{0}'.format(self.TCPPORT))
+    
+    #======================== public ==========================================
+    
+    #======================== private =========================================
+    
+    #=== web handles
+    
+    def _webhandle_root_GET(self):
+        return bottle.template(
+            'SimUI',
+            pagetitle   = 'DotBot Simulator',
+            version     = SimVersion.formatVersion(),
+        )
+    
+    def _webhandle_static_GET(self,filename):
+        return bottle.static_file(filename, root='static/')
+    
+    def _webhandle_floorplan_GET(self):
+        return self.floorplan.getJSON()
+    
+    def _webhandle_dotbots_GET(self):
+        simulatedTime = self.simEngine.currentTime()
+        
+        orchestratorView = self.orchestrator.getView()
+        returnVal = {
+            'mode':                self.simEngine.mode(),
+            'simulatedTime':       self.simEngine.formatSimulatedTime(),
+            'dotbots':             [],
+            'discomap':            orchestratorView['discomap'],
+        }
+        for dotbot in self.dotbots:
+            returnVal['dotbots'] += [dotbot.getAttitude()]
+        for (dotbot,orchestratorview) in zip(returnVal['dotbots'],orchestratorView['dotbots']):
+            dotbot['orchestratorview_x'] = orchestratorview['x']
+            dotbot['orchestratorview_y'] = orchestratorview['y']
+        return returnVal
+     
+    def _webhandle_frameforward_POST(self):
+        self.simEngine.commandFrameforward()
+     
+    def _webhandle_play_POST(self):
+        rxjson = bottle.request.json
+        self.simEngine.commandPlay(rxjson['speed'])
+    
+    def _webhandle_fastforward_POST(self):
+        self.simEngine.commandFastforward()
+    
+    def _webhandle_pause_POST(self):
+        self.simEngine.commandPause()
+    
+    #=== web server admin
+    
+    def _bottle_try_running_forever(self,*args,**kwargs):
+        RETRY_PERIOD = 3
+        while True:
+            try:
+                args[0](**kwargs) # blocking
+            except socket.error as err:
+                if err[0]==10013:
+                    print('FATAL: cannot open TCP port {0}.'.format(kwargs['port']))
+                    print('    Is another application running on that port?')
+                else:
+                    print(logError(err))
+            except Exception as err:
+                print(logError(err))
+            print('    Trying again in {0} seconds'.format(RETRY_PERIOD))
+            for _ in range(RETRY_PERIOD):
+                time.sleep(1)
+                print('.')
+            print('')
\ No newline at end of file
diff --git a/SimVersion.py b/SimVersion.py
new file mode 100644
index 0000000..d3a453d
--- /dev/null
+++ b/SimVersion.py
@@ -0,0 +1,6 @@
+MAJOR = 2
+MINOR = 0
+PATCH = 0
+
+def formatVersion():
+    return '{}.{}.{}'.format(MAJOR,MINOR,PATCH)
\ No newline at end of file
diff --git a/Utils.py b/Utils.py
new file mode 100644
index 0000000..75772ec
--- /dev/null
+++ b/Utils.py
@@ -0,0 +1,6 @@
+import math
+
+def distance(pos1,pos2):
+    (x1,y1) = pos1
+    (x2,y2) = pos2
+    return math.sqrt( (x1-x2)**2 + (y1-y2)**2 )
\ No newline at end of file
diff --git a/Wireless.py b/Wireless.py
new file mode 100644
index 0000000..a2a632f
--- /dev/null
+++ b/Wireless.py
@@ -0,0 +1,53 @@
+# built-in
+# third-party
+# local
+
+class Wireless(object):
+    '''
+    The wireless medium through which DotBot and orchestrator communicate.
+    '''
+    
+    PDR = 1
+    
+    # singleton pattern
+    _instance   = None
+    _init       = False
+    def __new__(cls, *args, **kwargs):
+        if not cls._instance:
+            cls._instance = super(Wireless, cls).__new__(cls, *args, **kwargs)
+        return cls._instance
+    
+    def __init__(self):
+        
+        # singleton patterm
+        if self._init:
+            return
+        self._init = True
+        
+        # local variables
+        self.dotbots      = None
+        self.orchestrator = None
+    
+    #======================== public ==========================================
+    
+    def indicateElements(self,dotbots,orchestrator):
+        assert self.dotbots==None
+        assert self.orchestrator==None
+        
+        self.dotbots      = dotbots
+        self.orchestrator = orchestrator
+    
+    def toDotBots(self,msg):
+        for dotbot in self.dotbots:
+            if self.PDR==1:
+                dotbot.fromOrchestrator(msg)
+            else:
+                raise NotImplementedError()
+    
+    def toOrchestrator(self,msg):
+        if self.PDR==1:
+            self.orchestrator.fromDotBot(msg)
+        else:
+            raise NotImplementedError()
+    
+    #======================== private =========================================
diff --git a/plots/README.md b/plots/README.md
deleted file mode 100644
index 2da73b6..0000000
--- a/plots/README.md
+++ /dev/null
@@ -1 +0,0 @@
-Plots appear here.
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 8248f97..7d34f73 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +1 @@
-matplotlib
-numpy
\ No newline at end of file
+bottle >= 0.12.18
\ No newline at end of file
diff --git a/static/README.md b/static/README.md
new file mode 100644
index 0000000..0ef8572
--- /dev/null
+++ b/static/README.md
@@ -0,0 +1 @@
+This directory contains the static files (images, etc) served by the bottle-based web server.
\ No newline at end of file
diff --git a/static/button_fastforward.svg b/static/button_fastforward.svg
new file mode 100644
index 0000000..abdaee1
--- /dev/null
+++ b/static/button_fastforward.svg
@@ -0,0 +1 @@
+<?xml version="1.0" ?><svg fill="none" height="24" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="2" viewBox="0 0 24 24" width="24" xmlns="http://www.w3.org/2000/svg"><polygon points="13 19 22 12 13 5 13 19"/><polygon points="2 19 11 12 2 5 2 19"/></svg>
\ No newline at end of file
diff --git a/static/button_frameforward.svg b/static/button_frameforward.svg
new file mode 100644
index 0000000..5c5614a
--- /dev/null
+++ b/static/button_frameforward.svg
@@ -0,0 +1 @@
+<?xml version="1.0" ?><svg height="24" viewBox="0 0 24 24" width="24" xmlns="http://www.w3.org/2000/svg"><polygon fill="none" points="5 4 15 12 5 20 5 4" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="2"/><line fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="2" x1="19" x2="19" y1="5" y2="19"/></svg>
\ No newline at end of file
diff --git a/static/button_pause.svg b/static/button_pause.svg
new file mode 100644
index 0000000..4c109c5
--- /dev/null
+++ b/static/button_pause.svg
@@ -0,0 +1 @@
+<?xml version="1.0" ?><svg height="24" viewBox="0 0 24 24" width="24" xmlns="http://www.w3.org/2000/svg"><rect fill="none" height="16" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="2" width="4" x="6" y="4"/><rect fill="none" height="16" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="2" width="4" x="14" y="4"/></svg>
\ No newline at end of file
diff --git a/static/button_play.svg b/static/button_play.svg
new file mode 100644
index 0000000..c73e351
--- /dev/null
+++ b/static/button_play.svg
@@ -0,0 +1 @@
+<?xml version="1.0" ?><svg height="24" viewBox="0 0 24 24" width="24" xmlns="http://www.w3.org/2000/svg"><circle cx="12" cy="12" fill="none" r="10" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="2"/><polygon fill="none" points="10 8 16 12 10 16 10 8" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="2"/></svg>
\ No newline at end of file
diff --git a/static/d3.min.js b/static/d3.min.js
new file mode 100644
index 0000000..0f3adbf
--- /dev/null
+++ b/static/d3.min.js
@@ -0,0 +1,2 @@
+// https://d3js.org v6.1.0 Copyright 2020 Mike Bostock
+!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?n(exports):"function"==typeof define&&define.amd?define(["exports"],n):n((t="undefined"!=typeof globalThis?globalThis:t||self).d3=t.d3||{})}(this,(function(t){"use strict";function n(t,n){return t<n?-1:t>n?1:t>=n?0:NaN}function e(t){let e=t,r=t;function i(t,n,e,i){for(null==e&&(e=0),null==i&&(i=t.length);e<i;){const o=e+i>>>1;r(t[o],n)<0?e=o+1:i=o}return e}return 1===t.length&&(e=(n,e)=>t(n)-e,r=function(t){return(e,r)=>n(t(e),r)}(t)),{left:i,center:function(t,n,r,o){null==r&&(r=0),null==o&&(o=t.length);const a=i(t,n,r,o-1);return a>r&&e(t[a-1],n)>-e(t[a],n)?a-1:a},right:function(t,n,e,i){for(null==e&&(e=0),null==i&&(i=t.length);e<i;){const o=e+i>>>1;r(t[o],n)>0?i=o:e=o+1}return e}}}function r(t){return null===t?NaN:+t}const i=e(n),o=i.right,a=i.left,u=e(r).center;function c(t,n){let e=0;if(void 0===n)for(let n of t)null!=n&&(n=+n)>=n&&++e;else{let r=-1;for(let i of t)null!=(i=n(i,++r,t))&&(i=+i)>=i&&++e}return 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2:this._point=3,this._x4=t,this._y4=n,this._context.moveTo((this._x0+4*this._x1+t)/6,(this._y0+4*this._y1+n)/6);break;default:Kb(this,t,n)}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=n}},tm.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._y0=this._y1=NaN,this._point=0},lineEnd:function(){(this._line||0!==this._line&&3===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1;break;case 1:this._point=2;break;case 2:this._point=3;var e=(this._x0+4*this._x1+t)/6,r=(this._y0+4*this._y1+n)/6;this._line?this._context.lineTo(e,r):this._context.moveTo(e,r);break;case 3:this._point=4;default:Kb(this,t,n)}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=n}},nm.prototype={lineStart:function(){this._x=[],this._y=[],this._basis.lineStart()},lineEnd:function(){var t=this._x,n=this._y,e=t.length-1;if(e>0)for(var 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3:this.point(this._x3,this._y3),this.point(this._x4,this._y4),this.point(this._x5,this._y5)}},point:function(t,n){if(t=+t,n=+n,this._point){var e=this._x2-t,r=this._y2-n;this._l23_a=Math.sqrt(this._l23_2a=Math.pow(e*e+r*r,this._alpha))}switch(this._point){case 0:this._point=1,this._x3=t,this._y3=n;break;case 1:this._point=2,this._context.moveTo(this._x4=t,this._y4=n);break;case 2:this._point=3,this._x5=t,this._y5=n;break;default:sm(this,t,n)}this._l01_a=this._l12_a,this._l12_a=this._l23_a,this._l01_2a=this._l12_2a,this._l12_2a=this._l23_2a,this._x0=this._x1,this._x1=this._x2,this._x2=t,this._y0=this._y1,this._y1=this._y2,this._y2=n}};var pm=function t(n){function e(t){return n?new dm(t,n):new am(t,0)}return e.alpha=function(n){return t(+n)},e}(.5);function 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arguments.length?(c[0]=+t[0],c[1]=+t[1],y):[c[0],c[1]]},y.translateExtent=function(t){return arguments.length?(f[0][0]=+t[0][0],f[1][0]=+t[1][0],f[0][1]=+t[0][1],f[1][1]=+t[1][1],y):[[f[0][0],f[0][1]],[f[1][0],f[1][1]]]},y.constrain=function(t){return arguments.length?(o=t,y):o},y.duration=function(t){return arguments.length?(s=+t,y):s},y.interpolate=function(t){return arguments.length?(l=t,y):l},y.on=function(){var t=h.on.apply(h,arguments);return t===h?y:t},y.clickDistance=function(t){return arguments.length?(p=(t=+t)*t,y):Math.sqrt(p)},y.tapDistance=function(t){return arguments.length?(g=+t,y):g},y},t.zoomIdentity=Im,t.zoomTransform=Bm,Object.defineProperty(t,"__esModule",{value:!0})}));
diff --git a/static/dotbotsim.css b/static/dotbotsim.css
new file mode 100644
index 0000000..1f173ae
--- /dev/null
+++ b/static/dotbotsim.css
@@ -0,0 +1,132 @@
+#pagetitle {
+    position:         absolute;
+    top:              10px;
+    left:             10px;
+    height:           40px;
+    font-style:       normal;
+    font-family:      Helvetica, sans-serif;
+    font-stretch:     condensed;
+    font-size:        40px;
+    text-align:       right;
+}
+
+#floorplan {
+    position:         absolute;
+    top:              60px;
+    left:             10px;
+    background-color: white;
+    border-width:     0px;
+    background-color: #f0f0f0;
+}
+
+#pausebutton {
+    position:         absolute;
+    left:             10px;
+    width:            50px;
+    height:           50px;
+    opacity:          50%;
+}
+
+#frameforwardbutton {
+    position:         absolute;
+    left:             80px;
+    width:            50px;
+    height:           50px;
+    opacity:          50%;
+}
+
+#playbuttontooltip {
+    position:         absolute;
+    left:             -100px;
+    width:            40px;
+    height:           20px;
+    opacity:          0.50;
+    border-style:     solid;
+    border-width:     1px;
+    border-color:     black;
+    background-color: #aaaaaa;
+    text-align:       center;
+    font-family:      Helvetica, sans-serif;
+}
+
+#playbuttonsliderdiv {
+    position:         absolute;
+    left:             150px;
+    width:            160px;
+    height:           50px;
+}
+
+#playbuttonslider {
+    position:         absolute;
+    left:             175px;
+    width:            110px;
+    height:           2px;
+    border-radius:    1px;
+    opacity:          0.10;
+    background-color: black;
+}
+
+#playbutton {
+    position:         absolute;
+    left:             150px;
+    width:            50px;
+    height:           50px;
+    opacity:          50%;
+}
+
+#fastforwardbutton {
+    position:         absolute;
+    left:             320px;
+    width:            50px;
+    height:           50px;
+    opacity:          50%;
+}
+
+#timelabel {
+    position:         absolute;
+    left:             380px;
+    width:            400px;
+    line-height:      50px;
+    font-family:      monospace;
+    text-align:       center;
+}
+
+#versionlabel {
+    position:         absolute;
+    width:            200px;
+    height:           50px;
+    text-align:       right;
+    font-family:      Helvetica, sans-serif;
+}
+
+.obstacle {
+    fill:             #aaaaaa;
+}
+
+.discomapdot {
+    fill:             blue;
+}
+
+.discomapline {
+    stroke-width:     4px;
+}
+
+.positionerror {
+    stroke:           red;
+    stroke-width:     2px;
+}
+
+.orchestratorview {
+    fill:             red;
+    stroke:           black;
+    stroke-width:     1px;
+}
+
+.collisionpath {
+    stroke-width:     1px;
+    stroke-dasharray: 2;
+}
+
+.dotbot {
+    stroke-width:     0px;
+}
diff --git a/static/dotbotsim.js b/static/dotbotsim.js
new file mode 100644
index 0000000..f8a3465
--- /dev/null
+++ b/static/dotbotsim.js
@@ -0,0 +1,273 @@
+var scaleFactor    = 1;
+var dotbotcolors   = d3.scaleOrdinal(d3.schemeCategory10).range();
+
+var playbuttonMinX     = 175
+var playbuttonMaxX     = 285
+var playbuttonMinSpeed =   1.00
+var playbuttonMaxSpeed =  10.00
+
+function coordinates2pixels(x,y) {
+    return (10*x,10*y)
+}
+
+function gettingThingsInPlace() {
+    // arming click events and tooltips on buttons
+    $("#pausebutton").click(function(){
+        $.post('pause')
+    });
+    $("#pausebutton").attr('title', 'Pause the simulation.');
+    $("#frameforwardbutton").click(function(){
+        $.post('frameforward')
+    });
+    $("#frameforwardbutton").attr('title', 'Advance the simulation by one event.');
+    $("#playbuttonsliderdiv").hover(handlerPlaybuttonsliderdivHoverIn,handlerPlaybuttonsliderdivHoverOut);
+    $("#playbuttonslider").hover(handlerPlaybuttonsliderdivHoverIn,handlerPlaybuttonsliderdivHoverOut);
+    $("#playbutton").mousedown(slideHandlerMouseDown);
+    $("#playbutton").attr('title', 'Drag to set the play speed.');
+    $("#playbutton").hover(handlerPlaybuttonsliderdivHoverIn,handlerPlaybuttonsliderdivHoverOut);
+    $("#fastforwardbutton").click(function(){
+        $.post('fastforward')
+    });
+    $("#fastforwardbutton").attr('title', 'Simulate as fast as possible.');
+}
+
+function getFloorplan() {
+    $.getJSON( "/floorplan.json", function( floorplan ) {
+        drawFloorplan(floorplan);
+    });
+}
+
+function drawFloorplan(floorplan) {
+    var svg = d3.select("#floorplan");
+    
+    // determine scalefactor such that map fill entire width of screen
+    scaleFactor = ($('body').innerWidth()-5) / floorplan.width;
+    
+    // scale map to fill up screen
+    svg.attr("width",  scaleFactor*floorplan.width)
+       .attr("height", scaleFactor*floorplan.height);
+    
+    // position walls
+    svg.selectAll("rect")
+        .data(floorplan.obstacles)
+        .enter().append("rect")
+            .attr("x",      function(d) { return scaleFactor*d.x; })
+            .attr("y",      function(d) { return scaleFactor*d.y; })
+            .attr("width",  function(d) { return scaleFactor*d.width; })
+            .attr("height", function(d) { return scaleFactor*d.height; })
+            .attr("class",  "obstacle");
+    
+    // position buttons and labels
+    $("#pagetitle").width(scaleFactor*floorplan.width);
+    $("#frameforwardbutton").offset(  { top: scaleFactor*floorplan.height+ 70 });
+    $("#fastforwardbutton").offset(   { top: scaleFactor*floorplan.height+ 70 });
+    $("#playbuttonsliderdiv").offset( { top: scaleFactor*floorplan.height+ 70 });
+    $("#playbuttonslider").offset(    { top: scaleFactor*floorplan.height+ 94 });
+    $("#playbuttontooltip").offset(   { top: scaleFactor*floorplan.height+ 45 });
+    $("#playbutton").offset(          { top: scaleFactor*floorplan.height+ 70 });
+    $("#pausebutton").offset(         { top: scaleFactor*floorplan.height+ 70 });
+    $("#timelabel").offset(           { top: scaleFactor*floorplan.height+ 70 });
+    $("#versionlabel").offset(
+        {
+            top:  scaleFactor*floorplan.height+ 70,
+            left: scaleFactor*floorplan.width - 190,
+        },
+    );
+}
+
+function getDotBots() {
+    $.getJSON( "/dotbots.json", function( data ) {
+        drawDotBots(data);
+    });
+}
+
+function drawDotBots(data) {
+    var svg    = d3.select("#floorplan");
+    
+    // mode
+    $("#pausebutton").css(        { opacity: data.mode=='pause'        ? 1.00 : 0.50 });
+    $("#frameforwardbutton").css( { opacity: data.mode=='frameforward' ? 1.00 : 0.50 });
+    $("#playbutton").css(         { opacity: data.mode=='play'         ? 1.00 : 0.50 });
+    $("#fastforwardbutton").css(  { opacity: data.mode=='fastforward'  ? 1.00 : 0.50 });
+    
+    // timelabel
+    $("#timelabel").html(data.simulatedTime);
+    
+    // positionerror
+    var positionerror  = svg.selectAll(".positionerror")
+        .data(data.dotbots);
+    positionerror
+        .attr("x1", function(d) { return scaleFactor*d.x; })
+        .attr("y1", function(d) { return scaleFactor*d.y; })
+        .attr("x2", function(d) { return scaleFactor*d.orchestratorview_x; })
+        .attr("y2", function(d) { return scaleFactor*d.orchestratorview_y; });
+    positionerror
+        .enter().append("line")
+            .attr("x1", function(d) { return scaleFactor*d.x; })
+            .attr("y1", function(d) { return scaleFactor*d.y; })
+            .attr("x2", function(d) { return scaleFactor*d.orchestratorview_x; })
+            .attr("y2", function(d) { return scaleFactor*d.orchestratorview_y; })
+            .attr("class", "positionerror");
+    
+    // orchestratorview
+    var orchestratorview = svg.selectAll(".orchestratorview")
+        .data(data.dotbots);
+    orchestratorview
+        .transition()
+            .attr("cx", function(d) { return scaleFactor*d.orchestratorview_x; })
+            .attr("cy", function(d) { return scaleFactor*d.orchestratorview_y; });
+    orchestratorview
+        .enter().append("circle")
+            .attr("cx", function(d) { return scaleFactor*d.orchestratorview_x; })
+            .attr("cy", function(d) { return scaleFactor*d.orchestratorview_y; })
+            .attr("class", "orchestratorview")
+            .attr("r", 6);
+    
+    // collisionpath
+    var collisionpaths  = svg.selectAll(".collisionpath")
+        .data(data.dotbots);
+    collisionpaths
+        .attr("x1", function(d) { return scaleFactor*d.x; })
+        .attr("y1", function(d) { return scaleFactor*d.y; })
+        .attr("x2", function(d) { return d.next_bump_x === null ? scaleFactor*d.x : scaleFactor*d.next_bump_x; })
+        .attr("y2", function(d) { return d.next_bump_y === null ? scaleFactor*d.y : scaleFactor*d.next_bump_y; });
+    collisionpaths
+        .enter().append("line")
+            .attr("x1", function(d) { return scaleFactor*d.x; })
+            .attr("y1", function(d) { return scaleFactor*d.y; })
+            .attr("x2", function(d) { return d.next_bump_x === null ? scaleFactor*d.x : scaleFactor*d.next_bump_x; })
+            .attr("y2", function(d) { return d.next_bump_y === null ? scaleFactor*d.y : scaleFactor*d.next_bump_y; })
+            .attr("class", "collisionpath")
+            .attr("stroke",function(d,i){return dotbotcolors[i%10];});
+    
+    // dotbots
+    var dotbots = svg.selectAll(".dotbot")
+        .data(data.dotbots);
+    dotbots
+        .transition()
+            .attr("cx", function(d) { return scaleFactor*d.x; })
+            .attr("cy", function(d) { return scaleFactor*d.y; });
+    dotbots
+        .enter().append("circle")
+            .attr("cx", function(d) { return scaleFactor*d.x; })
+            .attr("cy", function(d) { return scaleFactor*d.y; })
+            .attr("class", "dotbot")
+            .attr("fill",function(d,i){return dotbotcolors[i%10];})
+            .attr("r", 6);
+    
+    // discomapcomplete
+    if (data.discomap.complete==true) {
+        $(".discomapline").css( { stroke: 'green' } );
+    } else {
+        $(".discomapline").css( { stroke: 'red' } );
+    }
+    
+    // discomapdots
+    var discomapdots = svg.selectAll(".discomapdot")
+        .data(data.discomap.dots);
+    discomapdots
+        .attr("cx", function(d) { return scaleFactor*d[0]; })
+        .attr("cy", function(d) { return scaleFactor*d[1]; });
+    discomapdots
+        .enter().append("circle")
+            .attr("cx", function(d) { return scaleFactor*d[0]; })
+            .attr("cy", function(d) { return scaleFactor*d[1]; })
+            .attr("class", "discomapdot")
+            .attr("r", 2);
+    discomapdots
+        .exit()
+            .remove();
+    
+    // discomaplines
+    var discomaplines  = svg.selectAll(".discomapline")
+        .data(data.discomap.lines);
+    discomaplines
+        .attr("x1", function(d) { return scaleFactor*d[0]; })
+        .attr("y1", function(d) { return scaleFactor*d[1]; })
+        .attr("x2", function(d) { return scaleFactor*d[2]; })
+        .attr("y2", function(d) { return scaleFactor*d[3]; });
+    discomaplines
+        .enter().append("line")
+            .attr("x1", function(d) { return scaleFactor*d[0]; })
+            .attr("y1", function(d) { return scaleFactor*d[1]; })
+            .attr("x2", function(d) { return scaleFactor*d[2]; })
+            .attr("y2", function(d) { return scaleFactor*d[3]; })
+            .attr("class", "discomapline");
+    discomaplines
+        .exit()
+            .remove();
+}
+
+
+function handlerPlaybuttonsliderdivHoverIn(e) {
+    $("#playbuttonslider").css( { opacity: 0.30 } );
+}
+
+function handlerPlaybuttonsliderdivHoverOut(e) {
+    $("#playbuttonslider").css( { opacity: 0.10 } );
+}
+
+function slideHandlerMouseDown(e) {
+    
+    // avoid default event
+    e = e || window.event;
+    e.preventDefault();
+    
+    // arm handlers
+    document.onmousemove = slideHandlerMouseMove;
+    document.onmouseup   = slideHandlerMouseUp;
+}
+
+function slideHandlerMouseMove(e) {
+    
+    // avoid default event
+    e = e || window.event;
+    e.preventDefault();
+    
+    // record mouse position
+    newX = e.clientX;
+    
+    // move play button
+    if (newX<playbuttonMinX) {newX=playbuttonMinX};
+    if (newX>playbuttonMaxX) {newX=playbuttonMaxX};
+    $("#playbutton").offset({ left: newX-25 });
+    
+    // compute current speed setting
+    portion = (newX-playbuttonMinX) / (playbuttonMaxX-playbuttonMinX)
+    speed   = Math.round(playbuttonMinSpeed + portion*(playbuttonMaxSpeed-playbuttonMinSpeed));
+    
+    // display tootip
+    $("#playbuttontooltip").offset({ left: newX-20 });
+    $("#playbuttontooltip").html(speed+' x');
+}
+
+function slideHandlerMouseUp(e) {
+    
+    // avoid default event
+    e = e || window.event;
+    e.preventDefault();
+    
+    // disarm handlers
+    document.onmousemove = null;
+    document.onmouseup   = null;
+    
+    // remove tootip
+    $("#playbuttontooltip").html('');
+    $("#playbuttontooltip").offset({ left: -100 });
+    
+    
+    // determine speed
+    buttonX = $("#playbutton").position().left+25;
+    portion = (buttonX-playbuttonMinX) / (playbuttonMaxX-playbuttonMinX)
+    speed   = playbuttonMinSpeed + portion*(playbuttonMaxSpeed-playbuttonMinSpeed);
+    
+    // send command
+    $.ajax({
+        type:           "POST",
+        url:            'play',
+        contentType:    'application/json',
+        data:           JSON.stringify({
+            'speed':    speed,
+        })
+    });
+}
diff --git a/static/jquery-3.5.1.min.js b/static/jquery-3.5.1.min.js
new file mode 100644
index 0000000..b061403
--- /dev/null
+++ b/static/jquery-3.5.1.min.js
@@ -0,0 +1,2 @@
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diff --git a/static/ui.gif b/static/ui.gif
index 4590da3..197eeb8 100644
Binary files a/static/ui.gif and b/static/ui.gif differ
diff --git a/views/README.md b/views/README.md
new file mode 100644
index 0000000..e4b2f87
--- /dev/null
+++ b/views/README.md
@@ -0,0 +1 @@
+This directory contains template files used by the bottle-based web server.
\ No newline at end of file
diff --git a/views/SimUI.tpl b/views/SimUI.tpl
new file mode 100644
index 0000000..fdcc3b3
--- /dev/null
+++ b/views/SimUI.tpl
@@ -0,0 +1,33 @@
+<!doctype html>
+
+<meta charset="utf-8">
+<title>{{pagetitle}}</title>
+
+<link   rel="stylesheet" href="/static/dotbotsim.css">
+<script src="/static/d3.min.js"></script>
+<script src="/static/jquery-3.5.1.min.js" charset="utf-8"></script>
+<script src="/static/dotbotsim.js" charset="utf-8"></script>
+
+<div id="pagetitle"><strong>DOTBOT</strong>SIMULATOR</div>
+<svg id="floorplan"></svg>
+<div id="timelabel"></div>
+<div id="versionlabel">version {{version}}</div>
+<img id="pausebutton"        src="/static/button_pause.svg"/>
+<img id="frameforwardbutton" src="/static/button_frameforward.svg"/>
+<div id="playbuttontooltip"></div>
+<div id="playbuttonsliderdiv"></div>
+<div id="playbuttonslider"></div>
+<img id="playbutton"         src="/static/button_play.svg"/>
+<img id="fastforwardbutton"  src="/static/button_fastforward.svg"/>
+
+<script id="js">
+    $(document).ready(function() {
+        gettingThingsInPlace();
+        getFloorplan();
+        getDotBots();
+        // periodically refresh
+        setInterval(function() {
+            getDotBots()
+        }, 100);
+    });
+</script>
\ No newline at end of file